{"doi":"10.1111/epi.17974","title":"Reliable detection of generalized convulsive seizures using an off‐the‐shelf digital watch: A multisite phase 2 study","abstract":"OBJECTIVE: The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) \"fixed-and-frozen\" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS: LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the \"fixed-and-frozen\" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE: Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.","journal":"Epilepsia","year":2024,"id":429354,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8503,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":438150,"name":"Xiaojin Li","orcid":"0000-0003-2273-186X","position":1,"is_corresponding":false},{"id":721365,"name":"Jaison S. Hampson","orcid":null,"position":2,"is_corresponding":false},{"id":438152,"name":"Yan Huang","orcid":"0000-0002-6578-2379","position":3,"is_corresponding":false},{"id":533603,"name":"John C. Mosher","orcid":"0000-0002-3221-229X","position":4,"is_corresponding":false},{"id":977469,"name":"Yuri Dabaghian","orcid":"0000-0002-0272-7284","position":5,"is_corresponding":false},{"id":482866,"name":"Xi Luo","orcid":"0000-0002-1941-2318","position":6,"is_corresponding":false},{"id":951314,"name":"Blanca Talavera","orcid":"0000-0001-7692-0186","position":7,"is_corresponding":false},{"id":316361,"name":"Sandipan Pati","orcid":"0000-0002-9578-2820","position":8,"is_corresponding":false},{"id":1231988,"name":"Todd Masel","orcid":null,"position":9,"is_corresponding":false},{"id":768380,"name":"Ryan Hays","orcid":"0000-0002-3727-6225","position":10,"is_corresponding":false},{"id":431653,"name":"C. Ákos Szabó","orcid":"0000-0001-6731-3245","position":11,"is_corresponding":false},{"id":428368,"name":"Guo‐Qiang Zhang","orcid":"0000-0002-3663-1109","position":12,"is_corresponding":false},{"id":430844,"name":"Samden Lhatoo","orcid":"0000-0002-0260-3855","position":13,"is_corresponding":false},{"id":1231450,"name":"Yash Vakilna","orcid":"0000-0001-5084-9625","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-19T01:59:06.668462Z","pmid":"38738972","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}