Novel features for capturing temporal variations of rhythmic limb movement to distinguish convulsive epileptic and psychogenic nonepileptic seizures

Kusmakar, Shitanshu; Karmakar, Chandan; Yan, Bernard; Muthuganapathy, Ramanathan; Kwan, Patrick; O’Brien, Terence J.; Palaniswami, Marimuthu

doi:10.1111/epi.14619

Cited by 14 publications

(10 citation statements)

References 30 publications

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“…Based on these findings, in future we will investigate a more robust feature set such as, non-linear analysis of the multivariate time series data to capture patterns corresponding to seizures with duration up to 5 seconds. 19,20 However, based on the results of the proposed study it would be safe to assume that convulsive epileptic and nonepileptic seizures can be detected and differentiated non-invasively using wearable automated systems. Furthermore, the results of this study also validate and re-enforce the fact that convulsive PNES can be differentiated from convulsive ES by capturing the rhythmic movement activity during the event using movement-recording devices such as wearable accelerometers.…”

Section: Discussionmentioning

confidence: 99%

“…The automated algorithm classified these events as normal movements or activities of daily living, which was also expected as the algorithm uses features that were engineered for activities with duration ≥20 seconds. Based on these findings, in future we will investigate a more robust feature set such as, non‐linear analysis of the multivariate time series data to capture patterns corresponding to seizures with duration up to 5 seconds . However, based on the results of the proposed study it would be safe to assume that convulsive epileptic and non‐epileptic seizures can be detected and differentiated non‐invasively using wearable automated systems.…”

Section: Discussionmentioning

confidence: 99%

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The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non‐epileptic seizures

et al. 2019

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Objective Accurate differentiation between epileptic seizures (ES) and psychogenic non‐epileptic seizures (PNES) can be challenging based on history alone. Inpatient video EEG monitoring (VEM) is often needed for a definitive diagnosis. However, VEM is highly resource intensive, is of limited availability, and cannot be undertaken over long periods. Previous research has shown that time‐frequency analysis of accelerometer data could be utilized to differentiate between ES and PNES. Using a seizure detection and classification algorithm, we sought to examine the diagnostic utility of an automated analysis with an ambulatory accelerometer. Methods A wrist‐worn device was used to collect accelerometer data from patients during VEM admission, for diagnostic evaluation of convulsive seizures. An automated process, that involved the use of K‐means clustering and support vector machines, was used to detect and classify each seizure as ES or PNES. The results were compared with VEM diagnoses determined by epileptologists blinded to the accelerometer data. Results Twenty‐four convulsive seizures, consisting of at least 20 seconds of sustained continuous activity, recorded from 11 patients during inpatient VEM (13 PNES from five patients and 11 ES from six patients) were included for analysis. The automated system detected all convulsive seizures (ES, PNES) from >661 hours of recording with 67 false alarms (2.4 per 24 hours). The sensitivity and specificity for classifying ES from PNES were 72.7% and 100%, respectively. The positive and negative predictive values for classifying PNES were 81.3% and 100%, respectively. There was no significant difference between the classification results obtained from the automation process and the VEM diagnoses. Significance This automated system can potentially provide a wearable out‐of‐hospital seizure diagnostic monitoring system.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non‐epileptic seizures

et al. 2019

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“…wrist acceleration or surface EMG) limiting analyses to only convulsive seizures (e.g. [13], [14], [15]) or have focused only on epileptic seizure identification [16]. Likewise, studies of heart rate variability (HRV) have distinguished only convulsive ES and PNES [17].…”

Section: Introductionmentioning

confidence: 99%

Ictal autonomic activity recorded via wearable-sensors plus machine learning can discriminate epileptic and psychogenic nonepileptic seizures

Zsom

LaFrance

Blum

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Differentiating epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) is commonly based on electroencephalogram and concurrent video recordings (vEEG). Here, we demonstrate that these two types of seizures can be discriminated based on signals related to autonomic nervous system activity recorded via wearable sensors. We used Empatica E4 Wristband sensors worn on both arms in vEEG confirmed seizures, and machine learning methods to train classifiers, specifically, extreme gradient boosting (XGBoost). Classification performance achieved a predictive accuracy of 78 ± 1.5% on previously unseen data for whether a seizure was epileptic or psychogenic, which is 6 standard deviations above the baseline of 68% accuracy. Our dataset contained altogether 35 seizures from 18 patients out of which 8 patients had 13 convulsive seizures. Prediction of seizure type was based on simple features derived from the segments of autonomic activity measurements (electrodermal activity, body temperature, blood volume pulse, and heart rate) and forearm acceleration. Features related to heart rate and electrodermal activity were ranked as the top predictors in XGBoost classifiers. We found that patients with PNES had a higher ictal heart rate and electrodermal activity than patients with ES. In contrast to existing published studies of mainly convulsive seizures, our classifier focuses on autonomic signals to differentiate convulsive or nonconvulsive semiology ES from PNES. Our results show that autonomic activity recorded via wearable sensors provides promising signals for detection and discrimination of psychogenic and epileptic seizures, but more work is necessary to improve the predictive power of the model.

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“…However, shapelet extraction is computationally expensive and pose challenges for larger datasets. Future work in this direction include extending shape-iVAT (1) from learned shapelets instead of exhaustive search [20], (2) for identifying pathological motion patterns from wearable sensor data in neurological disorders such as stroke and epilepsy [14,15] and (3) for visualization of evolving clusters [1,5] through an incremental shape-iVAT for streaming data for long-term motion monitoring.…”

Section: Discussionmentioning

confidence: 99%

“…However, these realizations of VAT/iVAT for waveform datasets employ global distance measures, that fail to capture local characteristics. Local templates and shape characteristics that recur in human motion time-series can be representative of upper limb activities [3,13], motor impairments [14] as well as seizures [15]. Instead of using distances between entire time-series, distance to such patterns within the series can be used as local features for cluster exploration in such cases.…”

Section: Introductionmentioning

confidence: 99%

Shapelet Based Visual Assessment of Cluster Tendency in Analyzing Complex Upper Limb Motion

Datta

Karmakar

Rathore

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The evolution of ubiquitous sensors has led to the generation of copious amounts of waveform data. Human motion waveform analysis has found significance in clinical and home-based activity monitoring. Exploration of cluster structure in such waveform data prior to developing learning models is an important pattern recognition problem. A prominent category of algorithms in this direction, known as Visual Assessment of (cluster) Tendency (VAT), employs visual approaches to study cluster evolution through heat maps. This paper proposes shape-iVAT, a new relative of an improved VAT model, that captures local time-series characteristics through representative subsequences, known as shapelets, to identify interesting patterns in motion data. We propose an unsupervised method for shapelet extraction using maximin shape sampling and shapebased distance computation for selecting key shapelets representing characteristic motion patterns. These shapelets are used to transform waveform data into a dissimilarity matrix for VAT evaluation. We demonstrate that the proposed method outperforms standard VAT with global distance measures for identifying complex upper limb motion captured using a camera-based motion sensing device. We also show that our method has significance in efficient and interpretable cluster tendency assessment for anomaly detection and continuous motion monitoring.

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Novel features for capturing temporal variations of rhythmic limb movement to distinguish convulsive epileptic and psychogenic nonepileptic seizures

Cited by 14 publications

References 30 publications

The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non‐epileptic seizures

The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non‐epileptic seizures

Ictal autonomic activity recorded via wearable-sensors plus machine learning can discriminate epileptic and psychogenic nonepileptic seizures

Shapelet Based Visual Assessment of Cluster Tendency in Analyzing Complex Upper Limb Motion

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