System and method for processing and presenting arrhythmia information to facilitate heart arrhythmia identification and treatment
29 claims: 7 independent, 22 dependent
- 1マシン実施方法であって、 生体について得られた生理データ中の不整脈イベントを識別するステップを含み、前記識別した不整脈イベントは第1グループのデータを構成するものであり、さらに 少なくとも一部の前記不整脈イベントに対する人為評価を含む第2グループのデータを受信するステップと、 前記第1グループのデータと前記第2グループのデータとの間の少なくとも1つの相関の程度を判定するステップと、 この相関の程度に基づいて少なくとも一部の前記不整脈イベントに関する情報を選択的に表示するステップとを含む方法。
- 2不整脈イベントを識別するステップは、心房細動イベントを識別するステップを含み、 選択的に情報を表示するステップは、定義期間中の、前記生体に対する前記心房細動イベントおよび心拍数データに関する情報を、前記定義期間中の心房細動イベントの識別に対する高い肯定的予測を前記相関の程度が示す場合に、共通の時間スケールと併せて表示するステップを含む請求項1の方法。
- 3人為評価を受信するステップは、前記心房細動イベントのサブセットに対する人為評価を受信するステップを含み、 心房細動イベントを識別するステップは、複数の時間間隔内の前記生理データを検査するステップと、少なくとも1つの心房細動イベントが発生した前記間隔を識別するステップと、前記識別した間隔を報告するステップとを含む請求項2の方法。
- 4情報を表示するステップは、前記共通の時間スケール上に、前記心拍数データに関する前記情報の位置に合わせて、前記識別した間隔をディスプレイするステップを含む請求項3の方法。
- 5緊急または典型的な前記心房細動イベントのサブセットを識別するステップを更に含み、前記識別したサブセットは前記人為評価されるサブセットである請求項3の方法。
- 6前記人為評価と、前記識別したイベントとの間の相関の程度を判定するステップは、人為評価した不整脈イベントの少なくとも一部を含む幾つかの前記識別した間隔を、少なくとも時間データの比較に基づいて評価するステップを含む請求項3の方法。
- 7前記心拍数データに関する前記情報を表示するステップは、時間間隔での最大心拍数を含んだ心拍数傾向グラフをディスプレイするステップを含む請求項3の方法。
- 8前記各心拍数間隔を30分とし、前記各心房細動間隔を10分とする請求項7の方法。
- 9前記情報を表示するステップは、前記共通の時間スケールを用いて2つのグラフに情報をディスプレイするステップを含む請求項2の方法。
- 10前記情報を表示するステップは、前記共通の時間スケールを用いて単一のグラフに前記情報をディスプレイするステップを含む請求項2の方法。
- 11前記相関の程度に基づいて少なくとも一部の前記不整脈イベントに関する情報を選択的に表示するステップは、前記相関の程度が少なくとも1つの所定の値と一致するか、それを超える場合に、前記不整脈イベントに関する情報を選択的に表示するステップを含む請求項1の方法。
- 12前記相関の程度に基づいて少なくとも一部の前記不整脈イベントに関する情報を選択的に表示するステップは、前記相関の程度が少なくとも1つの所定の値と一致するか、それ未満の場合に、前記不整脈イベントに関する情報を選択的に表示するステップを含む請求項1の方法。
- 131つまたは複数のマシンにより実行されることで動作を生み出す命令を示す情報を具体化するマシン読取り可能な媒体を備える物品であって、 生体について得られた生理データ中の不整脈イベントを識別するステップを含み、前記識別した不整脈イベントは第1グループのデータを構成するものであり、さらに 少なくとも一部の前記不整脈イベントに対する人為評価を含む第2グループのデータを受信するステップと、 前記第1グループのデータと前記第2グループのデータとの間の少なくとも1つの相関の程度を判定するステップと、 この相関の程度に基づいて少なくとも一部の前記不整脈イベントに関する情報を選択的に表示するステップとを含む物品。
- 14不整脈イベントを識別するステップは、心房細動イベントを識別するステップを含み、 選択的に情報を表示するステップは、定義期間中の、前記生体に対する前記心房細動イベントおよび心拍数データに関する情報を、前記定義期間中の心房細動イベントの識別に対する高い肯定的予測を前記相関の程度が示す場合に、共通の時間スケールと併せて表示するステップを含む請求項13の物品。
- 15人為評価を受信するステップは、前記心房細動イベントのサブセットに対する人為評価を受信するステップを含み、 心房細動イベントを識別するステップは、複数の時間間隔内の前記生理データを検査するステップと、少なくとも1つの心房細動イベントが発生した前記間隔を識別するステップと、前記識別した間隔を報告するステップとを含む請求項14の物品。
- 16前記相関の程度に基づいて少なくとも一部の前記不整脈イベントに関する情報を選択的に表示するステップは、前記相関の程度が少なくとも1つの所定の値と一致するか、それを超える場合に、前記不整脈イベントに関する情報を選択的に表示するステップを含む請求項13の方法。
- 17前記相関の程度に基づいて少なくとも一部の前記不整脈イベントに関する情報を選択的に表示するステップは、前記相関の程度が少なくとも1つの所定の値と一致するか、それ未満の場合に、前記不整脈イベントに関する情報を選択的に表示するステップを含む請求項13の方法。
- 18不整脈イベントに関連する情報を報告するためのシステムであって、 生体についての生理データを処理して報告するように構成されるとともに、前記生理データから不整脈イベントを識別するように構成された監視システムと、 前記監視システムからの前記生理データを受信するための監視ステーションと、 不整脈情報を前記監視システムから受信し、少なくとも一部の前記生理データから導かれる人為評価した不整脈情報を前記監視ステーションから受信し、前記監視システムから受信した不整脈情報と前記人為評価した不整脈情報との少なくとも一つの相関の程度を判定し、かつ前記少なくとも一つの相関の程度に基づいて不整脈イベントに関する情報を報告するように構成された処理システムとを備えたシステム。
- 19前記処理システムは、定義期間中の心房細動イベントの前記識別に対する高い肯定的予測を前記相関の程度が示す場合に、前記定義期間中の、前記生体についての心房細動イベントおよび心拍数データに関する情報を共通の時間スケールと併せて表示できる請求項18のシステム。
- 20前記処理システムは、前記相関の程度が少なくとも1つの所定の値と一致するか、それを超える場合に、定義期間中の前記生体に対する心房細胞イベントおよび心拍数に関する情報を共通の時間スケールで共に表示することができるようにした請求項18のシステム。
- 21前記処理システムは、前記相関の程度が少なくとも1つの所定の値と一致するか、それ未満の場合に、定義期間中の前記生体に対する心房細胞イベントおよび心拍数に関する情報を共通の時間スケールで共に表示することができるようにした請求項18のシステム。
- 22不整脈イベントに関連する情報を報告するためのシステムであって、 生体についての、心拍数データを含む生理データを処理して報告するとともに、前記生理データから不整脈イベントを識別するための監視手段と、 前記生理データを前記監視手段から受信するとともに、前記生理データをユーザにディスプレイするためのディスプレイ手段と、 不整脈情報を前記監視システムから受信するとともに、人為評価した不整脈情報を前記ディスプレイ手段から受信するための処理手段とを備え、 前記人為評価した不整脈情報は、少なくとも一部の前記生理データから導かれ、 前記処理手段は、心拍数傾向を不整脈イベント負荷とともに表示するように、共通の時間スケールを用いて、定義期間中の前記心拍数データに関する情報および前記識別不整脈イベントに従った前記定義期間中の不整脈イベント活動持続時間に関する情報を図形表示できるシステム。
- 23前記監視手段は、複数の時間間隔内の前記生理データを検査し、少なくとも1つの心房細動イベントが発生した前記間隔を識別することができ、 前記処理システムは、前記識別した間隔を、前記共通の時間スケール上に前記心拍数データに関する前記情報の位置に合わせてディスプレイできる請求項22のシステム。
- 24マシン実施方法であって、 生体について心拍数データを取得するステップと、 前記生体について得られた生理データ中の心房細動イベントを識別するステップとを含み、前記識別した心房細動イベントは第1グループのデータを構成するものであり、心房細動イベントを識別するステップは、複数の時間間隔内の前記生理データを検査するステップ、および少なくとも1つの心房細動イベントが発生した前記間隔を識別するステップを含んでおり、さらに 少なくとも一部の前記心房細動イベントに対する人為評価を含む第2グループのデータを受信するステップと、 前記第1グループのデータと前記第2グループのデータとの間の少なくとも1つの相関の程度を判定するステップとを含み、少なくとも1つの相関の程度を判定するステップは、少なくとも時間データの比較に基づいて、少なくとも一部の前記人為評価した心房細動イベントを含む幾つかの前記識別した間隔を評価するステップを含んでおり、さらに 前記相関の程度が少なくとも1つの所定の値と一致するか、それ未満の場合に、心拍数傾向が心房細動負荷と並ぶように、共通の時間スケールを用いて、定義期間中の前記心拍数データに関する情報および前記識別心房細動イベントに従った前記定義期間中の心房細動活動持続時間に関する情報を図形表示するステップとを含み、図形表示するステップは、前記共通の時間スケール上に、前記心拍数データに関する前記情報の位置に合わせて前記識別間隔をディスプレイするステップを含んでなる方法。
- 25図形表示するステップは、定義期間中の、前記生体に対する前記心房細動イベントおよび心拍数データに関する情報を、前記定義期間中の心房細動イベントの前記識別に対する高い肯定的予測を前記相関の程度が示す場合に、共通の時間スケールと併せて表示するステップを含む請求項24の方法。
- 26生体について得られた生理データ中の不整脈イベントを識別するための手段を備え、前記識別した不整脈イベントは第1グループのデータを構成するものであり、さらに 少なくとも一部の前記不整脈イベントに対する人為評価を含む第2グループのデータを受信するための手段と、 前記第1グループのデータと前記第2グループのデータとの間の少なくとも1つの相関の程度を判定するための手段と、 前記相関の程度が少なくとも1つの所定の値と一致するか、それを超える場合に、この相関の程度に基づいて少なくとも一部の前記不整脈イベントに関する情報を選択的に表示するための手段とを備える装置。
- 27前記不整脈イベントは、心房細動イベントを含み、 選択的に表示するための前記手段は、定義期間中の、前記生体に対する前記心房細動イベントおよび心拍数データに関する情報を、前記定義期間中の心房細動イベントの前記識別に対する高い肯定的予測を前記相関の程度が示す場合に、共通の時間スケールと併せて表示することができる請求項26の装置。
- 28マシン実施方法であって、 生体について心拍数データを取得するステップと、 前記生体について得られた生理データ中の不整脈イベントを識別するステップとを含み、前記識別した不整脈イベントは第1グループのデータを構成するものであり、不整脈イベントを識別するステップは、複数の時間間隔内の前記生理データを検査するステップ、および少なくとも1つの不整脈イベントが発生した前記間隔を識別するステップを含んでおり、さらに 少なくとも一部の前記不整脈イベントに対する人為評価を含む第2グループのデータを受信するステップと、 前記第1グループのデータと前記第2グループのデータとの間の少なくとも1つの相関の程度を判定するステップとを含み、少なくとも1つの相関の程度を判定するステップは、少なくとも時間データの比較に基づいて、少なくとも一部の前記人為評価した不整脈イベントを含む幾つかの前記識別した間隔を評価するステップを含んでおり、さらに 前記相関の程度が少なくとも1つの所定の値と一致するか、それを超える場合に、心拍数傾向が不整脈イベント負荷と並ぶように、共通の時間スケールを用いて、定義期間中の前記心拍数データに関する情報および前記識別不整脈イベントに従った前記定義期間中の不整脈イベント活動持続時間に関する情報を図形表示するステップを含み、図形表示するステップは、前記共通の時間スケール上に、前記心拍数データに関する前記情報の位置に合わせて前記識別間隔をディスプレイするステップを含んでなる方法。
- 29図形表示するステップは、定義期間中の、前記生体に対する前記不整脈イベントおよび心拍数データに関する情報を、前記定義期間中の不整脈イベントの前記識別に対する高い肯定的予測を前記相関の程度が示す場合に、共通の時間スケールと併せて表示するステップを含む請求項28の方法。
Independent claims29
31 paragraphs, as filed
The present application describes systems and techniques for processing and displaying arrhythmia event information from physiological data, such as selective display of atrial fibrillation events to practitioners.
For many years, various devices have been used to monitor the living body's heart. In addition, systems have been used to collect and report cardiac information obtained from patients.
In one aspect, a cardiac surveillance system generally collects cardiac data from an individual being monitored and stores it in a surveillance center. It can process the collected data and display a graphic representation of the collected information to assist the practitioner in treating atrial arrhythmias such as atrial fibrillation. The system and method are such that the steps of identifying arrhythmia events in the physiological data obtained for the living body, the steps of receiving an artificial evaluation for at least a portion of the arrhythmia events, and the correlation between the artificial evaluation and the identified events. It can involve an action that includes a step of determining the degree and a step of selectively displaying information about the identified event based on the degree of correlation. The movement includes information on the step of identifying the atrial fibrillation event in the physiological data obtained for the living body, the step of acquiring the heart rate data for the living body, the heart rate data, and the duration of the atrial fibrillation event. , Can be displayed in conjunction with a common time scale and include a step to graphically represent heart rate trends along with atrial fibrillation load during the defined period.
One or more of the following advantages are feasible. Cardiac surveillance can be cycled every 24 hours and can automatically send cardiac data at least every 24 hours. The system can automatically generate a graphic overview of daily atrial fibrillation (AF) load for practitioners to consider and effectively display it wherever one or more communication networks are used. it can. AF load graphs can be used for asymptomatic AF detection, drug therapy (rate, rhythm, anticoagulant), pre / post ablation monitoring, and CHF (congestive heart failure) decompensation failure. The system can provide 96% of total sensitivity, 99% or more of positive predictions, and 90% or more of anthropogenic impact elimination. In one practice, the graph displays events only if AF detection is enabled for more than 50% of auto-identification events by an AF detection specialist.
According to one embodiment, the machine implementation technique involves identifying atrial fibrillation events in the physiological data obtained for the living body, acquiring heart rate data for the living body, and heart rate trends with atrial fibrillation load. Steps to graphically display information about heart rate data during the defined period and information about the duration of atrial fibrillation activity during the defined period according to the identified atrial fibrillation event, using a common time scale. And can be included. The step of graphically displaying the information can include displaying information about both the occurrence and duration of the atrial fibrillation event identified during the definition period. The heart rate data may be information displayed by the pulse per minute. The heart rate data may be information displayed in average pulse per minute or may include information about the standard deviation of heart rate.
The step of graphically displaying the information can include the step of displaying the heart rate tendency along with the atrial fibrillation load. The step of graphically displaying the information may include a step of displaying the heart rate tendency and the atrial fibrillation load on the same graph. The step of graphically displaying the information may include a step of displaying the heart rate tendency and the atrial fibrillation load in different graphs.
The step of identifying an atrial fibrillation event can include examining physiological data within a plurality of time intervals and identifying the interval at which at least one atrial fibrillation event has occurred, displaying information. The steps to be performed may include displaying the identified intervals in alignment with the location of information about heart rate data on a common time scale. The step of displaying information can include the step of selectively displaying information based on the degree of correlation between the identified atrial fibrillation event and the artificial evaluation for at least a part of the identified atrial fibrillation event. .. In addition, machine implementation techniques can include the step of receiving input specifying a definition period.
According to another aspect, the machine practice technique comprises identifying arrhythmia events in the physiological data obtained for the living body, the identified arrhythmia events constituting the first group of data, and at least one. The step of receiving the second group of data, including an artificial assessment of the part's arrhythmia event, the step of determining the degree of at least one correlation between the first group of data and the second group of data, and the degree of correlation. Can include a step of selectively displaying information about at least some arrhythmia events based on the degree of this correlation if is consistent with or exceeds at least one predetermined value. This selective display is a comparison of the degree of correlation with at least one predetermined value (eg, checking if the degree of correlation matches or exceeds at least one predetermined value, or at least matching or less than). Both checks are possible). The step of identifying an arrhythmia event can include a step of identifying an atrial fibrillation event, and a step of selectively displaying information provides information on the atrial fibrillation event and heart rate data for the living body during the definition period. , Can include the step of displaying in conjunction with a common time scale, where the degree of correlation indicates a high positive prediction for the identification of atrial fibrillation events during the defined period.
The step of receiving an artificial evaluation can include the step of receiving an artificial evaluation for a subset of atrial fibrillation events, and the step of identifying an atrial fibrillation event is a step of examining physiological data within multiple time intervals. , A step of identifying the interval at which at least one atrial fibrillation event has occurred and a step of reporting the identified interval can be included. The step of displaying the information can include a step of displaying the identified intervals in alignment with the position of the information about the heart rate data on a common time scale. The technique can also include identifying a subset of urgent or typical atrial fibrillation events, the identified subset being an artificially evaluated subset. The step of determining the degree of correlation between the artificial evaluation and the identified event is to evaluate some identified intervals including at least a part of the artificially evaluated arrhythmia event based on at least a comparison of time data. Can be included.
The step of displaying information about heart rate data can include displaying a heart rate trend graph including the maximum heart rate at time intervals. Each heart rate interval may be 30 minutes and each atrial fibrillation interval may be 10 minutes. The step of displaying information can include the step of displaying information on two graphs using a common time scale. The step of displaying information can include the step of displaying information on a single graph using a common time scale.
According to another aspect, the system for reporting information related to arrhythmic events is configured to process and report physiological data about the living body and to identify arrhythmic events from the physiological data. The monitoring system, the monitoring station for receiving physiological data from the monitoring system, and the arrhythmia information are configured to be received from the monitoring system, and the artificially evaluated arrhythmia information is received from the monitoring station. The processing system can include an artificially evaluated arrhythmia information derived from at least some physiological data, and the processing system has a degree of correlation between the arrhythmia information from the monitoring system and the artificially evaluated arrhythmia information. , Report information about arrhythmic events if they match or exceed a given value. The processing system shares information about atrial fibrillation events and heart rate data for the living body during the definition period when the degree of correlation shows a high positive prediction for the identification of atrial fibrillation events during the definition period. Has the ability to display along with the scale.
According to another aspect, the system for reporting information related to the arrhythmia event is configured to process and report physiological data including heart rate data about the living body, and also to report the arrhythmia event from the physiological data. A monitoring system configured to identify, a monitoring station for receiving physiological data from the monitoring system, a monitoring station configured to receive arrhythmia information from the monitoring system, and an artificially evaluated arrhythmia information. The processing system can include a processing system configured to receive from, the artificially evaluated arrhythmia information is derived from at least some physiological data, and the processing system displays the heart rate tendency together with the arrhythmia event load. , Information on heart rate data during the defined period and information on the duration of arrhythmia event activity during the defined period according to the identified arrhythmia event can be graphically displayed using a common time scale. A monitoring system can examine physiological data within multiple time intervals to identify the interval at which at least one atrial fibrillation event has occurred, and a processing system can heartbeat the identified interval on a common time scale. It has the ability to display information about numerical data according to its position.
According to another aspect, the machine implementation technique comprises the step of acquiring heart rate data for the living body and the step of identifying the arrhythmia event in the physiological data obtained for the living body, and the identified arrhythmia event is the first. It constitutes the data of the group, and the step of identifying the arrhythmia event includes the step of examining the physiological data within a plurality of time intervals and the step of identifying the interval in which at least one arrhythmia event occurs. In addition, the step of receiving the data of the second group including the artificial evaluation for at least some arrhythmia events, and the step of determining the degree of at least one correlation between the data of the first group and the data of the second group. And at least one step of determining the degree of correlation includes assessing several identified intervals, including at least some anthropogenic arrhythmia events, based on a comparison of time data. In addition, if the degree of correlation matches or exceeds at least one predetermined value, information about the heart rate data during the defined period, using a common time scale, so that the heart rate tendency aligns with the arrhythmia event load. And, including the step of graphically illustrating information about the duration of arrhythmia event activity during the defined period according to the identified arrhythmia event, the graphical steps align the information about the heart rate data on a common time scale. It comprises the step of displaying the identified intervals.
According to another aspect, the machine practice technique comprises identifying arrhythmia events in the physiological data obtained for the living body, the identified arrhythmia events constituting the first group of data, and at least one. The step of receiving the second group of data, including an artificial assessment of the part's arrhythmia event, the step of determining the degree of at least one correlation between the first group of data and the second group of data, and the degree of correlation. Selective information, including the step of selectively displaying information about at least some of the identified arrhythmia events based on the degree of this correlation if is consistent with or exceeds at least one predetermined value. The steps displayed in show common time information about arrhythmia events and heart rate data identified for the living body during the definition period, when the degree of correlation shows a high positive prediction for the identification of arrhythmia events during the definition period. Includes steps to display along with the scale.
The systems and techniques described above can be performed using articles containing machine-readable media that embody information indicating the instructions that produce the described actions when performed by one or more machines. Details of one or more embodiments will be described in the accompanying drawings and the following description. Other features and advantages will be apparent from such statements, drawings and claims.
<figref num="1">FIG. 1 shows a system for reporting information related to an arrhythmia event, according to an exemplary embodiment.</figref><figref num="2">FIG. 2 is a graph showing an example of atrial fibrillation load and heart rate tendency according to one embodiment.</figref><figref num="3">FIG. 3 illustrates a procedure for monitoring, processing, and reporting information associated with an arrhythmia event, according to an exemplary embodiment.</figref><figref num="4">FIG. 4 shows a graph showing an example of atrial fibrillation load and a graph showing an example of heart rate tendency according to an exemplary embodiment.</figref><figref num="5">FIG. 5 illustrates a procedure for monitoring, processing, and reporting information associated with an arrhythmia event, according to another exemplary embodiment.</figref><figref num="6">FIG. 6 illustrates a procedure for monitoring, processing, and reporting information associated with an arrhythmia event, according to another exemplary embodiment.</figref>
FIG. 1 shows a system for reporting information related to an arrhythmia event, such as an atrial fibrillation event, according to one embodiment. In this embodiment, the monitoring system 109 can communicate ECG (ECG), heart disease events, and other data to the monitoring center 104 (via devices 101 and 102). The system 109 can include, for example, an implantable medical device (IMD) such as an implantable cardiac fibrillation remover and associated transmitter / receiver or pacemaker and associated transmitter / receiver, or a monitoring device 101 worn by patient 110. Further, the monitoring system 109 can include a monitoring processing device 102 capable of transmitting standard physiological data (received from the monitoring device 101) to the monitoring center 104 and detecting an arrhythmia event (such as an atrial fibrillation event). In one implementation, devices 101 and 102 are integrated into a single device. In addition, System 109 is available, for example, from CardioNet, Inc. of San Diego, CA, CardioNet Mobile Cardiac. It can be performed using an Outpatient Telemetry (MCOT) device.
The monitoring processing device 102 transfers physiological data (including data related to arrhythmia events) to a local area network (LAN), a terrestrial communication line telephone network, a wireless network, a satellite communication network, or a communication network which is another suitable network. It can be transmitted via 103 to facilitate bidirectional communication with the monitoring center 104. Advantageously, the monitoring center 104 may be located in the same location as the monitoring system 109 (eg, in the same room or building), or it may be located somewhere remote.
The monitoring center 104 can include a monitoring (or display) station 105 and a processing system 106. In one practice, a cardiovascular specialist (CVT) may use the monitoring station 105 to evaluate the physiological data received from the monitoring system 109 and identify and report arrhythmia events (such as atrial fibrillation events) in particular. it can. The CVT reports these physiological data assessments to the processing system 106, which also receives information related to the arrhythmia event identified by the monitoring system 109. As further described below, processing system 106 analyzes this arrhythmia event data (both artificially evaluated data from the CVT and data reported by monitoring system 109) and graphs (or graphs) associated with these events. Determine if other similar expressions) should be generated. In certain situations, the processing system sends reports related to both arrhythmia and heart rate data, for example to a doctor or other healthcare provider 108, via line 107-which may be part of network 103. Will be sent.
FIG. 3 shows a procedure for monitoring, processing, and reporting arrhythmia event data (data associated with atrial fibrillation events, etc.) according to one embodiment. In this embodiment, the monitoring system 109 (shown in FIG. 1) monitors and reports physiological data (including heart rate related data) at 301. At 302, various parts of this physiological data are analyzed (eg, RR variability, QRS complex) and arrhythmia events are identified based on predetermined criteria. Information related to these events (although there are other possible information) constitutes the first group of data. In one embodiment, the monitoring system 109 identifies some emergency or typical arrhythmia events and reports these events to both the CVT at 303 and the processing system at 304. Alternatively, the system may simply report the event identified in 302 to the processing system. In addition, at 303, the CVT uses station 105 to evaluate various parts of the physiological data received from 302 and / or 301 and identify arrhythmia events, but these artificially evaluated events (and other thoughts). The information related to (although there is information that can be obtained) constitutes the data of the second group. If desired here, the CVT can request additional data from monitoring system 109.
At 304, the processing system 106 analyzes both the data in the first group and the data in the second group to determine the degree of correlation between the two groups. This step can include, for example, determining whether the degree of correlation exceeds and / or is equal to a predetermined correlation parameter, or is less than and / or equal to that parameter. If, based on the correlation analysis, the information associated with the arrhythmia event is determined to be valid, the system at 305, such as the graph shown in Figure 2 or the graph shown in Figure 4, heart rate trends and arrhythmia events. Generate reports related to both. On the other hand, if the correlation is inadequate, the system will not generate a report and will continue to monitor.
In one implementation, every 10 minutes is adopted for illustration, and if the monitoring system 109 detects one atrial fibrillation (AF) event during the last 10 minutes, it sends one "flag". In this practice, if more than 50% of the multiple flags (generated at 302) during the 10 minutes match the event identified by the CVT (at 303), that is, with respect to the period in question. ) Only if the correlation shows a high positive prediction for the identification of AF events, the processing system 106 is associated with heart rate trends and atrial fibrillation load, such as the graph shown in FIG. 2 or the graph shown in FIG. Generate one graph (or multiple graphs). If this 50% threshold is not met, the system simply continues data processing without generating a single graph (or multiple graphs) based on the data in question.
The term "atrial fibrillation load" (more generally, "arrhythmia event load") is a condition in which a patient has atrial fibrillation (or arrhythmia) over a specific period of time, taking into account the number and duration of attacks. Generally refers to the total amount of time in. Graphical displays such as those in Figures 2 and 4 are advantageous for practitioners to know if patients often experience arrhythmias such as AF at specific times of the day, which is a case. Some can affect the method of treatment.
Figure 2 illustrates an example of how to graphically represent both heart rate trends and atrial fibrillation loads on a common time scale (although graphs are essential to "graphicalize" such data. is not it). Graph 205 contains, for example, information related to daily AF occurrence and time of occurrence (time zone) 201, AF duration 202, and heart rate (reference numeral 203 and reference numeral 204). Scale 204 (in this example) indicates heart rate at the average pulse rate per minute, and the points and lines (for illustration) indicated by sign 203 are the values on that scale, the standard associated with these values. Shows deviation and heart rate during AF. In addition, Graph 205 shows heart rate data after 15 and 45 minutes per hour. Finally, in this graph, even if there is one or more AF events in a given 10 minutes, it is graphed at 10-minute intervals.
Similar to FIG. 2, FIG. 4 shows an example of a method of graphically displaying heart rate tendency and atrial fibrillation load on a common time scale. Figure 4 uses two graphs, unlike Figure 2, but displays the same information as in Figure 2. In particular, graphs 404 and 405 include information associated with, for example, daily AF occurrence and occurrence (time zone) 401, AF duration 402, and heart rate (reference numerals 403 and 406). Scale 406 (in this example) indicates heart rate at the average pulse rate per minute, and the points and lines (for illustration) indicated by sign 403 are the values on that scale, the standard associated with these values. Shows deviation and heart rate during AF.
5 and 6 are diagrams illustrating another embodiment of the present invention. Specifically, at 501, system 111 uses monitoring system 109 to acquire physiological data, including heart rate data. Then, at 502, the system identifies the presence of an arrhythmia event (such as an AF event) in this physiological data while examining the organized data within multiple time intervals. At 503, the system assigns multiple flags indicating the presence of arrhythmia events and reports these multiple flags to the processing system, which make up the data in the first group. Similarly, at 504, the system identifies and reports physiological data, such as ECG data, for a subset of events identified at 502 and reported at 503. It should be noted that in this implementation the system does not need to report physiological data for each flag assigned in 503, but only data related to the most prominent event identified in 502. This means that the data sent to the CVT is minimized.
At 601, the CVT analyzes this data and reports whether an arrhythmia event has occurred, which produces the second group of data. The processing system then (in 602) has at least one correlation between the data in the first group and the data in the second group, based on a comparison of the time stamps (time data) associated with the data in each group. Determine the degree. For the sake of explanation, if enough artificially evaluated events reported in 601 match multiple events reported in 503, the system is data valid, i.e. identifying arrhythmia events. Judge that there is a high positive prediction for. When such a determination is made, the data associated with each flag reported in 503 is graphically displayed in the format shown in FIGS. 2 and 4. It should be noted that this graphic representation in this embodiment can include all such data, but the CVT only needs to consider a subset of this data. In other words, the system improves the accuracy of the representation of information related to arrhythmia events while minimizing the data that the CVT considers.
All of the disclosed systems and the functional operations illustrated and described herein can be performed on digital electronic circuits, i.e., computer hardware, firmware, software, or a combination thereof. The device can be executed by a programmable processor, can be carried out by a software product (eg, a computer program product) that is clearly embodied in a machine-readable storage device, and the processing operations are performed by a programmable processor that executes an instruction program. The function can be executed by manipulating the input data and generating the output. Further, it is advantageous to implement the system with one or more software programs that can be run on the programmable system. The programmable system can include: That is, 1) at least one programmable processor connected to receive data and instructions from the data storage system and send the data and instructions to the data storage system, 2) at least one input device, and 3) at least. One output device. In addition, each software program can be implemented in a high-level procedural or object-oriented program language, or assembly or machine language if desired, in which case the language can be a compiled or interpreted language. it can.
Also, by way of example, suitable processors include general purpose and dedicated microprocessors. In general, the processor receives instructions and data from read-only memory, random access memory, and / or machine-readable signals (eg, digital signals received over a network connection). In general, a computer will include one or more mass storage devices for storing data files. Such devices include magnetic disks such as internal hard disks and removable disks, magneto-optical disks, and optical disks. Suitable storage devices for clearly embodying software program instructions and data include all forms of non-volatile memory, which are merely examples, including: That is, 1) semiconductor memory devices such as EPROM (electronic programmable read-only memory) and EEPROM (electronic erasable programmable read-only memory), and flash memory devices; 2) magnetic disks such as internal hard disks and removable disks; 3) Magneto-optical disk; and 4) CD-ROM disk. Any of the above may be complemented or incorporated into an ASIC (Application Specific Integrated Circuit).
A display device such as a monitor or LCD (liquid crystal display) screen for displaying information to the user to provide interaction with the user (such as a CVT), and a keyboard for the user to provide input to a computer system. , And a pointing device such as a mouse or trackball, the system can be implemented on a computer system. The computer program may be programmed to provide a graphic user interface for interaction with the user.
Finally, the system has described a particular embodiment, but other embodiments are within the scope of the following claims. For example, the disclosed actions can be performed in different orders and still achieve the desired result. In addition, the system does not have to utilize 10 minute intervals and can have many different time intervals (such as no intervals at all), including 1 minute, 30 seconds, and 30 minute intervals. In fact, since no time interval is required, the graphs in FIGS. 2 and 4 can be transformed to show continuous heart rate trends (with corresponding AF data) rather than heart rate trends for a particular instance. In addition, FIGS. 2 and 4 show an example of graphically (especially) displaying an atrial fibrillation load (a type of arrhythmia event load), but may also display the same or similar information for another type of arrhythmia event. it can. In fact, it is possible to graphically display information related to several different types of arrhythmia event loads using both the formats and procedures associated with generating Figure 2 or Figure 4 (or similar figures). it can.
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| JP10243930A | Cites | Japan |
| JP04300523A | Cites | Japan |
| JP63099840A | Cites | Japan |
| JP2003000559A | Cites | Japan |
| JP2000195910A | Cites | Japan |
| JP2003130815A | Cites | Japan |
| JP08206089A | Cites | Japan |
24 members in 6 offices
Priority claims2
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| 60525386 | United States of America | – | |
| 52538603 | United States of America | P |
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| AU2004305423A1 | Australia | A1 | |
| CA2544926A1 | Canada | A1 | |
| CA2683198A1 | Canada | A1 | |
| WO2005060829A1 | World Intellectual Property Organization (WIPO) | A1 | |
| EP1691683A1 | European Patent Office (EPO) | A1 | |
| US7212850B2 | United States of America | B2 | |
| JP2007516024A | Japan | A | |
| US2007191723A1 | United States of America | A1 | |
| AU2004305423B2 | Australia | B2 | |
| JP2010029683A | Japan | A | |
| JP2010188148A | Japan | A | |
| EP1691683A4 | European Patent Office (EPO) | A4 | |
| US7907996B2 | United States of America | B2 | |
| JP4674212B2 | Japan | B2 | |
| US2011166468A1 | United States of America | A1 | |
| JP4944934B2This record | Japan | B2 | |
| JP4944973B2 | Japan | B2 | |
| CA2544926C | Canada | C | |
| EP1691683B1 | European Patent Office (EPO) | B1 | |
| US8945019B2 | United States of America | B2 | |
| US2015190067A1 | United States of America | A1 | |
| CA2683198C | Canada | C | |
| US10278607B2 | United States of America | B2 |
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Numbers
- Publication
- 4944934
- Application
- 226039
Titles2
- Japanese
- 心臓不整脈の識別および治療を容易にするために不整脈情報を処理して、表示するシステムおよび方法
- English
- Systems and methods for processing and displaying arrhythmia information to facilitate identification and treatment of cardiac arrhythmias
Classification
- CPC, 7
- A61B5/0245
- A61B5/361
- A61B5/0006
- A61B5/339
- A61B5/7246
- A61B5/7282
- A61B5/742
- IPC, 6
- A61B5 0452
- A61B5 0402
- A61B5 363
- A61B5 00
- A61B5 0245
- A61B5 361
