Boyu Zhang, Weixuan V. Chen, Giulia Regalia, Daniel M. Goldenholz, Rosalind W. Picard
{"title":"来自日常生活中使用的医疗智能手表的大规模客观强直阵挛发作记录的统计特征","authors":"Boyu Zhang, Weixuan V. Chen, Giulia Regalia, Daniel M. Goldenholz, Rosalind W. Picard","doi":"10.1111/epi.18109","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>This study aimed to assess whether population-level patterns in seizure occurrence previously observed in self-reported diaries, medical records, and electroencephalographic recordings were also present in tonic–clonic seizure (TCS) diaries produced via the combined input of a US Food and Drug Administration-cleared wristband with an artificial intelligence detection algorithm and patient self-reports. We also investigated the characteristics of patient interactions with wearable seizure alerts.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We analyzed wristband data from patients with TCSs who had at least three reported TCSs over a minimum of 90 days. We quantified TCS frequency and cycles, and the relationship between the mean and variability of monthly TCS counts. We also assessed interaction metrics such as false alarm dismissal and seizure confirmation rates.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Applying strict criteria for usable data, we reviewed 137 490 TCSs from 3012 patients, with a median length of TCS alert records of 445 days (range = 90–1806). Analyses showed consistency between prior diary studies and the present data concerning (1) the distribution of monthly TCS frequency (median = 3.1, range = .08–26); (2) the linear relationship (slope = .79, <i>R</i><sup>2</sup> = .83) between the logarithm of the mean and the logarithm of the SD of monthly TCS frequency (L-relationship); and (iii) the prevalence of multiple coexisting seizure cycles, including circadian (84.0%), weekly (24.6%), and long-term cycles (31.1%).</p>\n </section>\n \n <section>\n \n <h3> Significance</h3>\n \n <p>Key population-level patterns in seizure occurrence are recapitulated in wrist-worn device recordings, supporting their validity for tracking TCS burden. Compared to other approaches, wearables can provide noninvasive, objective, long-term data, revealing cycles in seizure risk. However, improved patient engagement with wristband alerts and further validation of detection accuracy in ambulatory settings are needed. Together, these findings suggest that data from smart wristbands may be used to derive features of TCS records and, ultimately, facilitate remote monitoring and the development of personalized forecasting tools for TCS management. Our findings may not generalize to other types of seizures.</p>\n </section>\n </div>","PeriodicalId":11768,"journal":{"name":"Epilepsia","volume":"65 11","pages":"3255-3264"},"PeriodicalIF":6.6000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/epi.18109","citationCount":"0","resultStr":"{\"title\":\"Statistical characteristics of large-scale objective tonic–clonic seizure records from medical smartwatches used in daily life\",\"authors\":\"Boyu Zhang, Weixuan V. Chen, Giulia Regalia, Daniel M. Goldenholz, Rosalind W. Picard\",\"doi\":\"10.1111/epi.18109\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>This study aimed to assess whether population-level patterns in seizure occurrence previously observed in self-reported diaries, medical records, and electroencephalographic recordings were also present in tonic–clonic seizure (TCS) diaries produced via the combined input of a US Food and Drug Administration-cleared wristband with an artificial intelligence detection algorithm and patient self-reports. We also investigated the characteristics of patient interactions with wearable seizure alerts.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We analyzed wristband data from patients with TCSs who had at least three reported TCSs over a minimum of 90 days. We quantified TCS frequency and cycles, and the relationship between the mean and variability of monthly TCS counts. We also assessed interaction metrics such as false alarm dismissal and seizure confirmation rates.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Applying strict criteria for usable data, we reviewed 137 490 TCSs from 3012 patients, with a median length of TCS alert records of 445 days (range = 90–1806). Analyses showed consistency between prior diary studies and the present data concerning (1) the distribution of monthly TCS frequency (median = 3.1, range = .08–26); (2) the linear relationship (slope = .79, <i>R</i><sup>2</sup> = .83) between the logarithm of the mean and the logarithm of the SD of monthly TCS frequency (L-relationship); and (iii) the prevalence of multiple coexisting seizure cycles, including circadian (84.0%), weekly (24.6%), and long-term cycles (31.1%).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Significance</h3>\\n \\n <p>Key population-level patterns in seizure occurrence are recapitulated in wrist-worn device recordings, supporting their validity for tracking TCS burden. Compared to other approaches, wearables can provide noninvasive, objective, long-term data, revealing cycles in seizure risk. However, improved patient engagement with wristband alerts and further validation of detection accuracy in ambulatory settings are needed. Together, these findings suggest that data from smart wristbands may be used to derive features of TCS records and, ultimately, facilitate remote monitoring and the development of personalized forecasting tools for TCS management. 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Statistical characteristics of large-scale objective tonic–clonic seizure records from medical smartwatches used in daily life
Objective
This study aimed to assess whether population-level patterns in seizure occurrence previously observed in self-reported diaries, medical records, and electroencephalographic recordings were also present in tonic–clonic seizure (TCS) diaries produced via the combined input of a US Food and Drug Administration-cleared wristband with an artificial intelligence detection algorithm and patient self-reports. We also investigated the characteristics of patient interactions with wearable seizure alerts.
Methods
We analyzed wristband data from patients with TCSs who had at least three reported TCSs over a minimum of 90 days. We quantified TCS frequency and cycles, and the relationship between the mean and variability of monthly TCS counts. We also assessed interaction metrics such as false alarm dismissal and seizure confirmation rates.
Results
Applying strict criteria for usable data, we reviewed 137 490 TCSs from 3012 patients, with a median length of TCS alert records of 445 days (range = 90–1806). Analyses showed consistency between prior diary studies and the present data concerning (1) the distribution of monthly TCS frequency (median = 3.1, range = .08–26); (2) the linear relationship (slope = .79, R2 = .83) between the logarithm of the mean and the logarithm of the SD of monthly TCS frequency (L-relationship); and (iii) the prevalence of multiple coexisting seizure cycles, including circadian (84.0%), weekly (24.6%), and long-term cycles (31.1%).
Significance
Key population-level patterns in seizure occurrence are recapitulated in wrist-worn device recordings, supporting their validity for tracking TCS burden. Compared to other approaches, wearables can provide noninvasive, objective, long-term data, revealing cycles in seizure risk. However, improved patient engagement with wristband alerts and further validation of detection accuracy in ambulatory settings are needed. Together, these findings suggest that data from smart wristbands may be used to derive features of TCS records and, ultimately, facilitate remote monitoring and the development of personalized forecasting tools for TCS management. Our findings may not generalize to other types of seizures.
期刊介绍：
Epilepsia is the leading, authoritative source for innovative clinical and basic science research for all aspects of epilepsy and seizures. In addition, Epilepsia publishes critical reviews, opinion pieces, and guidelines that foster understanding and aim to improve the diagnosis and treatment of people with seizures and epilepsy.
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