Jesper Jeppesen, Jakob Christensen, Oliver Ahrenfeldt Petersen, Sarah Fenger, Sidsel Armand Larsen, Stephan Wüstenhagen, Stefan Rahr Wagner, Peter Johansen, Sándor Beniczky
{"title":"使用连接到智能手机的可穿戴心电图检测癫痫发作:一项3期临床验证研究。","authors":"Jesper Jeppesen, Jakob Christensen, Oliver Ahrenfeldt Petersen, Sarah Fenger, Sidsel Armand Larsen, Stephan Wüstenhagen, Stefan Rahr Wagner, Peter Johansen, Sándor Beniczky","doi":"10.1016/j.ebiom.2025.105952","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Automated seizure detection is needed for patient safety and for objective seizure quantification. Wearable seizure detection devices hold great potential to improve patient care. Our objectives were to assess the accuracy of a wearable ECG-device connected to a smartphone, in detecting epileptic seizures in patients with autonomic ictal changes, and evaluate its capability to automatically determine impairment of consciousness.</p><p><strong>Methods: </strong>We conducted a phase 3, prospective, blinded, multicentre, clinical validation study of real-time seizure detection using a predefined algorithm. We recruited consecutive patients admitted to Epilepsy Monitoring Units. Eligible patients experienced seizures with autonomic ictal manifestations, defined as ictal heart rate change exceeding 50 beats per minute, inferred from the first recorded seizure. Patients wore an ECG-device connected to a smartphone. The algorithm, based on heart rate variability, used a personalised detection threshold determined from the first 24 h of recording. During daytime, seizure detection triggered automated behavioural-testing on the smartphone to confirm detection and assess consciousness.</p><p><strong>Findings: </strong>Of 101 enrolled patients, 36 experienced seizures, with 42 seizures recorded from 17 eligible patients. Overall sensitivity across all 42 seizures was 90·5% (95% CI: 77·4-97·3%), median sensitivity per patient was 100% (95% CI: 100-100%). All bilateral tonic-clonic seizures were detected, while sensitivity for other focal seizures was 82·6% (95% CI: 61·2-95·1%), median per patient: 100% (95% CI: 60-100%). Mean false alarm rate was 2·5/day (median per patient: 1·1/day, 95% CI: 0-2·8/day, zero during the night). Device deficiency time was 1·8% and signal loss was 4·5% (median per patient: 0·3% and 0·5% respectively). Use of the behavioural-testing application successfully cancelled all false alarms and accurately identified impairment of consciousness.</p><p><strong>Interpretation: </strong>The wearable ECG device connected to a smartphone accurately detected focal and generalised seizures, and assessed impairment of consciousness.</p><p><strong>Funding: </strong>Independent Research Fund Denmark (grant number 0134-00400B).</p>","PeriodicalId":11494,"journal":{"name":"EBioMedicine","volume":"120 ","pages":"105952"},"PeriodicalIF":10.8000,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Seizure detection using wearable electrocardiogram connected to a smartphone: a phase 3 clinical validation study.\",\"authors\":\"Jesper Jeppesen, Jakob Christensen, Oliver Ahrenfeldt Petersen, Sarah Fenger, Sidsel Armand Larsen, Stephan Wüstenhagen, Stefan Rahr Wagner, Peter Johansen, Sándor Beniczky\",\"doi\":\"10.1016/j.ebiom.2025.105952\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Automated seizure detection is needed for patient safety and for objective seizure quantification. Wearable seizure detection devices hold great potential to improve patient care. Our objectives were to assess the accuracy of a wearable ECG-device connected to a smartphone, in detecting epileptic seizures in patients with autonomic ictal changes, and evaluate its capability to automatically determine impairment of consciousness.</p><p><strong>Methods: </strong>We conducted a phase 3, prospective, blinded, multicentre, clinical validation study of real-time seizure detection using a predefined algorithm. We recruited consecutive patients admitted to Epilepsy Monitoring Units. Eligible patients experienced seizures with autonomic ictal manifestations, defined as ictal heart rate change exceeding 50 beats per minute, inferred from the first recorded seizure. Patients wore an ECG-device connected to a smartphone. The algorithm, based on heart rate variability, used a personalised detection threshold determined from the first 24 h of recording. During daytime, seizure detection triggered automated behavioural-testing on the smartphone to confirm detection and assess consciousness.</p><p><strong>Findings: </strong>Of 101 enrolled patients, 36 experienced seizures, with 42 seizures recorded from 17 eligible patients. Overall sensitivity across all 42 seizures was 90·5% (95% CI: 77·4-97·3%), median sensitivity per patient was 100% (95% CI: 100-100%). All bilateral tonic-clonic seizures were detected, while sensitivity for other focal seizures was 82·6% (95% CI: 61·2-95·1%), median per patient: 100% (95% CI: 60-100%). Mean false alarm rate was 2·5/day (median per patient: 1·1/day, 95% CI: 0-2·8/day, zero during the night). Device deficiency time was 1·8% and signal loss was 4·5% (median per patient: 0·3% and 0·5% respectively). 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Seizure detection using wearable electrocardiogram connected to a smartphone: a phase 3 clinical validation study.
Background: Automated seizure detection is needed for patient safety and for objective seizure quantification. Wearable seizure detection devices hold great potential to improve patient care. Our objectives were to assess the accuracy of a wearable ECG-device connected to a smartphone, in detecting epileptic seizures in patients with autonomic ictal changes, and evaluate its capability to automatically determine impairment of consciousness.
Methods: We conducted a phase 3, prospective, blinded, multicentre, clinical validation study of real-time seizure detection using a predefined algorithm. We recruited consecutive patients admitted to Epilepsy Monitoring Units. Eligible patients experienced seizures with autonomic ictal manifestations, defined as ictal heart rate change exceeding 50 beats per minute, inferred from the first recorded seizure. Patients wore an ECG-device connected to a smartphone. The algorithm, based on heart rate variability, used a personalised detection threshold determined from the first 24 h of recording. During daytime, seizure detection triggered automated behavioural-testing on the smartphone to confirm detection and assess consciousness.
Findings: Of 101 enrolled patients, 36 experienced seizures, with 42 seizures recorded from 17 eligible patients. Overall sensitivity across all 42 seizures was 90·5% (95% CI: 77·4-97·3%), median sensitivity per patient was 100% (95% CI: 100-100%). All bilateral tonic-clonic seizures were detected, while sensitivity for other focal seizures was 82·6% (95% CI: 61·2-95·1%), median per patient: 100% (95% CI: 60-100%). Mean false alarm rate was 2·5/day (median per patient: 1·1/day, 95% CI: 0-2·8/day, zero during the night). Device deficiency time was 1·8% and signal loss was 4·5% (median per patient: 0·3% and 0·5% respectively). Use of the behavioural-testing application successfully cancelled all false alarms and accurately identified impairment of consciousness.
Interpretation: The wearable ECG device connected to a smartphone accurately detected focal and generalised seizures, and assessed impairment of consciousness.
Funding: Independent Research Fund Denmark (grant number 0134-00400B).
EBioMedicineBiochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
17.70
自引率
0.90%
发文量
579
审稿时长
5 weeks
期刊介绍:
eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.