Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients.
Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients.
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DOI：
10.1161/circheartfailure.117.004313
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发表时间：
2018-01
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影响因子：
--
通讯作者：
Klein L
中科院分区：
文献类型：
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作者：
Inan OT;Baran Pouyan M;Javaid AQ;Dowling S;Etemadi M;Dorier A;Heller JA;Bicen AO;Roy S;De Marco T;Klein L
使用可穿戴设备对心力衰竭（HF）患者进行远程监测可以允许对治疗进行患者特定的调整，从而潜在地减少住院治疗。我们的目的是评估HF状态使用可穿戴测量的电气和机械方面的心脏功能在运动的背景下。患有代偿性（门诊）和失代偿性（住院）HF的患者配有可穿戴心电图（ECG）和心震图（SCG）传感贴片。患者在休息时进行初始记录，进行6分钟步行试验（6MWT），然后休息5分钟恢复。该方案在门诊访视时或HF住院期间的两个时间点（入院和出院）进行。为了评估患者状态，我们设计了一种方法，该方法基于使用图形挖掘（图形相似性评分，GSS）比较运动后与休息后SCG信号结构的相似性。我们发现GSS可以评估HF患者的状态，并与45例患者（13例失代偿，32例代偿）的临床改善相关。两组之间的GSS指标存在显著差异（44.4±4.9 [失代偿性HF] vs. 35.2±10.5 [代偿性HF]，p<0.001）。在6例失代偿患者的纵向数据中，我们发现GSS从入院（失代偿）到出院（代偿）有显著变化（44±4.1 [入院] vs. 35±3.9 [出院]，p<0.05）。记录心脏功能的可穿戴技术和机器学习算法可以通过分析心脏对次极量运动的反应来评估代偿性和失代偿性HF状态。这些技术可以在未来进行测试，以跟踪HF门诊患者的临床状态及其对药物干预的反应。
Remote monitoring of heart failure (HF) patients using wearable devices can allow patient-specific adjustments to treatments and thereby potentially reduce hospitalizations. We aimed to assess HF state using wearable measurements of electrical and mechanical aspects of cardiac function in the context of exercise. Patients with compensated (outpatient) and decompensated (hospitalized) HF were fitted with a wearable electrocardiogram (ECG) and seismocardiogram (SCG) sensing patch. Patients stood at rest for an initial recording, performed a six-minute walk test (6MWT), and then stood at rest for five minutes of recovery. The protocol was performed at the time of outpatient visit or at two time points (admission and discharge) during an HF hospitalization. To assess patient state, we devised a method based on comparing the similarity of the structure of SCG signals following exercise compared to rest using graph mining (Graph Similarity Score, GSS). We found that GSS can assess HF patient state, and correlates to clinical improvement in 45 patients (13 decompensated, 32 compensated). A significant difference was found between the groups in the GSS metric (44.4±4.9 [Decompensated HF] vs. 35.2±10.5 [Compensated HF], p<0.001). In the six decompensated patients with longitudinal data we found a significant change in GSS from admission (decompensated) to discharge (compensated) (44±4.1 [Admitted] vs. 35±3.9 [Discharged], p<0.05). Wearable technologies recording cardiac function and machine learning algorithms can assess compensated and decompensated HF states by analyzing cardiac response to sub-maximal exercise. These techniques can be tested in the future to track the clinical status of outpatients with HF and their response to pharmacological interventions.