Photoplethysmography: A measure for the function of the autonomic nervous system in focal impaired awareness seizures

Atrache, Rima El; Tamilia, Eleonora; Touserkani, Fatemeh Mohammadpour; Hammond, Sarah P.; Papadelis, Christos; Kapur, Kush; Jackson, Michele; Bucciarelli, Bethany; Tsuboyama, Melissa; Sarkis, Rani A.; Loddenkemper, Tobias

doi:10.1111/epi.16621

Cited by 11 publications

(11 citation statements)

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“…Given the sensitivity of BVP to motion, which may result in artifactual signals, 34,38 the seizure detection performance of BVP may improve in seizures with a limited motor component. BVP may therefore assist in the detection of the peri‐ictal period of tonic–clonic seizures and focal unaware seizures 39,40 . EDA with or without ACC has shown promise in the evaluation of convulsive seizures, 26,30,41 and may also be able to detect focal seizures 41 .…”

Section: Discussionmentioning

confidence: 99%

“…BVP may therefore assist in the detection of the peri‐ictal period of tonic–clonic seizures and focal unaware seizures. 39 , 40 EDA with or without ACC has shown promise in the evaluation of convulsive seizures, 26 , 30 , 41 and may also be able to detect focal seizures. 41 In our study, EDA's overall AUC‐ROC of .549 is not significantly better than chance, but EDA AUC‐ROC values reached .802, .699, and .662 for GTCSs, focal automatisms, and FBTCSs.…”

Section: Discussionmentioning

confidence: 99%

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Seizure detection using wearable sensors and machine learning: Setting a benchmark

Tang

Atrache

et al. 2021

Epilepsia

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Objective Tracking seizures is crucial for epilepsy monitoring and treatment evaluation. Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may miss seizures. Wearable devices may be better tolerated and more suitable for long‐term ambulatory monitoring. This study evaluates the seizure detection performance of custom‐developed machine learning (ML) algorithms across a broad spectrum of epileptic seizures utilizing wrist‐ and ankle‐worn multisignal biosensors. Methods We enrolled patients admitted to the epilepsy monitoring unit and asked them to wear a wearable sensor on either their wrists or ankles. The sensor recorded body temperature, electrodermal activity, accelerometry (ACC), and photoplethysmography, which provides blood volume pulse (BVP). We used electroencephalographic seizure onset and offset as determined by a board‐certified epileptologist as a standard comparison. We trained and validated ML for two different algorithms: Algorithm 1, ML methods for developing seizure type‐specific detection models for nine individual seizure types; and Algorithm 2, ML methods for building general seizure type‐agnostic detection, lumping together all seizure types. Results We included 94 patients (57.4% female, median age = 9.9 years) and 548 epileptic seizures (11 066 h of sensor data) for a total of 930 seizures and nine seizure types. Algorithm 1 detected eight of nine seizure types better than chance (area under the receiver operating characteristic curve [AUC‐ROC] = .648–.976). Algorithm 2 detected all nine seizure types better than chance (AUC‐ROC = .642–.995); a fusion of ACC and BVP modalities achieved the best AUC‐ROC (.752) when combining all seizure types together. Significance Automatic seizure detection using ML from multimodal wearable sensor data is feasible across a broad spectrum of epileptic seizures. Preliminary results show better than chance seizure detection. The next steps include validation of our results in larger datasets, evaluation of the detection utility tool for additional clinical seizure types, and integration of additional clinical information.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Seizure detection using wearable sensors and machine learning: Setting a benchmark

Tang

Atrache

et al. 2021

Epilepsia

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“…At present, a clear evidence gap has still to be filled before introducing the automated ambulatory detection of nonconvulsive seizures into clinical practice (28,78,79). However, promising results using the E4 wristband indicated that this may be possible with a wrist-worn device (80)(81)(82). Additionally, advanced post-processing analytics on the peri-ictal periods may provide seizure semiology information, thereby expanding the quality of available patient data.…”

Section: Future Research Directionsmentioning

confidence: 99%

Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit

Regalia¹,

Caborni²,

LaFrance

et al. 2021

Front. Neurol.

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Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs).Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration (“Active mode”).Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6–20 years, and 67 adult aged 21–63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89–1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87–1.73]), higher (p < 0.001) than in the adult population (0.57, CI: [0.36–0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (p < 0.001).Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.

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“…Most seizures are accompanied by change in heart rate (HR), which is extracted from ECG. 41,42 More importantly, change of HR may be anterior to clinical or EEG representations. 41,43 In general, the pulse rate measured by PPG is equal to the HR.…”

Section: Autonomic Nervous Activity In Seizuresmentioning

confidence: 99%

“…Adjusted by brain neurons, the autonomic nervous system also shows unusual status during seizures, mainly studied by ECG, PPG, and EDA. Most seizures are accompanied by change in heart rate (HR), which is extracted from ECG 41,42 . More importantly, change of HR may be anterior to clinical or EEG representations 41,43 .…”

Section: The Current State In the Field Of Seizure Detection Based On...mentioning

confidence: 99%

Seizure detection based on wearable devices: A review of device, mechanism, and algorithm

Wang

Lei

et al. 2022

Acta Neuro Scandinavica

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With sudden and unpredictable nature, seizures lead to great risk of the secondary damage, status epilepticus, and sudden unexpected death in epilepsy. Thus, it is essential to use a wearable device to detect seizure and inform patients' caregivers for assistant to prevent or relieve adverse consequence. In this review, we gave an account of the current state of the field of seizure detection based on wearable devices from three parts: devices, physiological activities, and algorithms. Firstly, seizure monitoring devices available in the market primarily involve wristband‐type devices, patch‐type devices, and armband‐type devices, which are able to detect motor seizures, focal autonomic seizures, or absence seizures. Secondly, seizure‐related physiological activities involve the discharge of brain neurons presented, autonomous nervous activities, and motor. Plenty of studies focus on features from one signal, while it is a lack of evidences about the change of signal coupling along with seizures. Thirdly, the seizure detection algorithms developed from simple threshold method to complicated machine learning and deep learning, aiming at distinguish seizures from normal events. After understanding of some preliminary studies, we will propose our own thought for future development in this field.

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Photoplethysmography: A measure for the function of the autonomic nervous system in focal impaired awareness seizures

Cited by 11 publications

References 37 publications

Seizure detection using wearable sensors and machine learning: Setting a benchmark

Seizure detection using wearable sensors and machine learning: Setting a benchmark

Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit

Seizure detection based on wearable devices: A review of device, mechanism, and algorithm

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