Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features

Fujiwara, Koichi; Miyajima, Miho; Yamakawa, Toshitaka; Abe, Erika; Suzuki, Yoko; Sawada, Yukihiro; Kano, Manabu; Maehara, Taketoshi; Ohta, Katsuya; Sasai-Sakuma, Taeko; Sasano, Tetsuo; Matsuura, Masato; Matsushima, Eisuke

doi:10.1109/tbme.2015.2512276

Cited by 129 publications

(73 citation statements)

References 30 publications

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“…None of the included studies validated an algorithm based on oxygen saturation or EDA alone. Most studies were conducted in adults, but two studies included a pediatric population [ 23 , 40 ], and six studies included both children and adults [ 22 , 25 , 35 – 37 , 39 ]. Fourteen studies prospectively enrolled their participants [ 8 , 22 , 23 , 26 , 28 , 30 – 33 , 36 – 40 ], but only two studies prospectively validated their algorithm [ 31 , 33 ].…”

Section: Resultsmentioning

confidence: 99%

“… Patient-specific heuristic adaptive classifier Retrospective 1) Children (14) 2) Other group of children (14) 107/695 h GOS (30), FOS (77) NA Patient-independent: Sens: (overall:81.3%) PPV: NA FAR (overall: 0.75/h) DL: NA Patient-specific: Sens: (overall: 77.6%) PPV: (overall: 30.7%) FAR (overall: 0.33/h) DL: 19.1 s De Cooman et al [ 24 ] Cardiac HRV Single-lead ECG See De Cooman [ 22 ]. Patient-specific heuristic adaptive classifier and real-time adaptive classifier Retrospective Temporal lobe epilepsy (19) 153/2833 h FOS, FOIA, FOBTC, U, subclinical NA Patient-independent: Sens: (overall: 78.4%) PPV: (overall: 2.4%) FAR: (overall: 1.73/h) DL: NA Patient-specific: Sens: (overall: 76.5%) PPV: (overall: 3.7%) FAR: (overall: 1.09/h) DL: NA Adaptive: Sens: (overall: 77.1%) PPV: (overall: 3.3%) FAR: (overall: 1.24/h) DL: NA Fujiwara et al [ 25 ] Cardiac HRV ECG Time domain analysis : mean NN, SDNN, RMSSD, TP, NN50. Frequency domain analysis : LF, HF, LF/HF.…”

Section: Resultsmentioning

confidence: 99%

“…All four performance measures (sensitivity, PPV, FAR, and DL) were only reported in three out of 21 studies [ 22 , 33 , 39 ]; eight studies reported three [ 2 , 23 – 25 , 28 , 30 , 31 , 42 ], eight studies reported two [ 8 , 26 , 34 , 36 – 38 , 40 , 43 ], one study reported one [ 41 ], and one study only reported sensitivity and PPV data for some of the subjects [ 32 ].…”

Section: Resultsmentioning

confidence: 99%

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Ictal autonomic changes as a tool for seizure detection: a systematic review

et al. 2018

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Purpose Adequate epileptic seizure detection may have the potential to minimize seizure-related complications and improve treatment evaluation. Autonomic changes often precede ictal electroencephalographic discharges and therefore provide a promising tool for timely seizure detection. We reviewed the literature for seizure detection algorithms using autonomic nervous system parameters. Methods The PubMed and Embase databases were systematically searched for original human studies that validate an algorithm for automatic seizure detection based on autonomic function alterations. Studies on neonates only and pilot studies without performance data were excluded. Algorithm performance was compared for studies with a similar design (retrospective vs. prospective) reporting both sensitivity and false alarm rate (FAR). Quality assessment was performed using QUADAS-2 and recently reported quality standards on reporting seizure detection algorithms. Results Twenty-one out of 638 studies were included in the analysis. Fifteen studies presented a single-modality algorithm based on heart rate variability ( n = 10), heart rate ( n = 4), or QRS morphology ( n = 1), while six studies assessed multimodal algorithms using various combinations of HR, corrected QT interval, oxygen saturation, electrodermal activity, and accelerometry. Most studies had small sample sizes and a short follow-up period. Only two studies performed a prospective validation. A tendency for a lower FAR was found for retrospectively validated algorithms using multimodal autonomic parameters compared to those using single modalities (mean sensitivity per participant 71–100% vs. 64–96%, and mean FAR per participant 0.0–2.4/h vs. 0.7–5.4/h). Conclusions The overall quality of studies on seizure detection using autonomic parameters is low. Unimodal autonomic algorithms cannot reach acceptable performance as false alarm rates are still too high. Larger prospective studies are needed to validate multimodal automatic seizure detection. Electronic supplementary material The online version of this article (10.1007/s10286-018-0568-1) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Ictal autonomic changes as a tool for seizure detection: a systematic review

et al. 2018

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“…Each false positive rate was calculated by applying the model to the relevant set of samples (based on the ECG recordings from the interictal periods and healthy subjects). Given previous results based on the use of EEG and ECG recordings (Park et al, 2011; Fujiwara et al, 2016), we defined the acceptable false positive rate as a rate of <0.5 h −1 .…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, Varon et al (2014) used ECG signals to achieve a positive predictive value (PPV) of 86.2% and a sensitivity of 100% for partial seizures, and a PPV of 84.3% and a sensitivity of 93.1% for generalized seizures. More recently, researchers described a multivariate statistical process for epileptic seizure prediction with a sensitivity of 91% and false positive rate of 0.7 h −1 (Fujiwara et al, 2016). …”

Section: Introductionmentioning

confidence: 99%

Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics

et al. 2017

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Epilepsy is a neurological disorder that causes changes in the autonomic nervous system. Heart rate variability (HRV) reflects the regulation of cardiac activity and autonomic nervous system tone. The early detection of epileptic seizures could foster the use of new treatment approaches. This study presents a new methodology for the prediction of epileptic seizures using HRV signals. Eigendecomposition of HRV parameter covariance matrices was used to create an input for a support vector machine (SVM)-based classifier. We analyzed clinical data from 12 patients (9 female; 3 male; age 34.5 ± 7.5 years), involving 34 seizures and a total of 55.2 h of interictal electrocardiogram (ECG) recordings. Data from 123.6 h of ECG recordings from healthy subjects were used to test false positive rate per hour (FP/h) in a completely independent data set. Our methodological approach allowed the detection of impending seizures from 5 min to just before the onset of a clinical/electrical seizure with a sensitivity of 94.1%. The FP rate was 0.49 h−1 in the recordings from patients with epilepsy and 0.19 h−1 in the recordings from healthy subjects. Our results suggest that it is feasible to use the dynamics of HRV parameters for the early detection and, potentially, the prediction of epileptic seizures.

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