Yoko Suzuki

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

Objective: The present study proposes a new epileptic seizure prediction method through integrating heart rate variability (HRV) analysis and an anomaly monitoring technique. Methods: Because excessive neuronal activities in the preictal period of epilepsy affect the autonomic nervous systems and autonomic nervous function affects HRV, it is assumed that a seizure can be predicted through monitoring HRV. In the proposed method, eight HRV features are monitored for predicting seizures by using multivariate statistical process control, which is a well-known anomaly monitoring method. Results: We applied the proposed method to the clinical data collected from 14 patients. In the collected data, 8 patients had a total of 11 awakening preictal episodes and the total length of interictal episodes was about 57 h. The application results of the proposed method demonstrated that seizures in ten out of eleven awakening preictal episodes could be predicted prior to the seizure onset, that is, its sensitivity was 91%, and its false positive rate was about 0.7 times per hour. Conclusion: This study proposed a new HRV-based epileptic seizure prediction method, and the possibility of realizing an HRV-based epileptic seizure prediction system was shown. Significance: The proposed method can be used in daily life, because the heart rate can be measured easily by using a wearable sensor.

Heart rate variability features for epilepsy seizure prediction

Although refractory epileptic patients suffer from uncontrolled seizures, their quality of life (QoL) may be improved if an epileptic seizure can be predicted in advance. In the preictal period, an excessive neuronal activity of epilepsy affects the autonomic nerve system. Since the fluctuation of the R-R interval (RRI) of an electrocardiogram (ECG), called heart rate variability (HRV), reflects the autonomic nervous function, an epileptic seizure may be predicted through monitoring HRV data of an epileptic patient. In the present work, preictal and interictal HRV data of epileptic patients were analyzed for developing an epilepsy seizure prediction system. The HRV data of five patients were collected, and their HRV features were calculated. The analysis results showed that frequency HRV features, such as LF and LF/HF, changed at least one minute before seizure onset in all seizure episodes. The possibility of realizing a HRV-based seizure prediction system was shown through these analysis.

A Triphasic Change of Cardiac Autonomic Nervous System During Electroconvulsive Therapy.

Objectives Dynamic autonomic activity changes have been repeatedly reported during electroconvulsive therapy (ECT). However, the specific timing of these changes remains unclear. To clarify whether sympathetic or parasympathetic nervous activity contributes separately to the second stage and the third stage during and after induced seizures by ECT, we examined heart rate (HR) and spectral analysis of variability (HRV) during ECT. Methods Seventeen patients with depression participated in the study and underwent ECT. The R-R intervals (RRI) were recorded and analyzed sequentially for the HRV indices high-frequency (HF) (an index of parasympathetic activity) and low-frequency (LF)/high-frequency (an index of sympathetic activity) for 4 minutes before and after stimulus onset by the maximum entropy method. Averaged HRs were compared between 3 heart beats prestimulus and poststimulus onset. The HRV power in the range of 30 to 80 and 80 to 130 seconds after a seizure was compared between the HF and LF/HF components. Results There was a significant reduction of the averaged HR over 3 HRs just after stimulus onset, suggesting parasympathetic dominance in the first phase. The LF/HF power significantly increased in the 30 to 80 s range after stimulus onset, whereas the HF power significantly increased in the 80 to 130 s range after stimulus onset, reflecting sympathetic activation in the second phase and parasympathetic activation in the third phase, respectively. Conclusions The evaluation of HR and HRV revealed a triphasic change from parasympathetic to sympathetic to parasympathetic cardiac autonomic activity after ECT stimulus onset in depression patients.

Development of a wearable HRV telemetry system to be operated by non-experts in daily life

A telemetry system for the measurement of heart rate variability (HRV) with automatic gain control has been developed with a low-cost manufacturing process and a low-power consumption design. The proposed automatic gain control technique provided highly reliable RR interval (RRI) detection for subjects of different ages, and enabled the subjects to use the system without any expert knowledge of the electrocardiogram (ECG) measurement. All the components and functions for the RRI measurement were implemented on a wearable telemeter which can operate for up to 440 h with a CR2032 coin battery, and the wirelessly transmitted RRI data is stored into a PC by a receiver via a USB connection. The errors of the RRI detection occurred at less than 2% probability in subjects of five different ages. In a long-term measurement of a young subject that extended over 48 h, the results showed a 0.752% probability of recurring errors. The obtained results suggest that the proposed system enables the long-term monitoring of HRV for both clinical care and healthcare operated by a non-expert.

Epileptic Seizure Monitoring by Using Multivariate Statistical Process Control

Abstract Although refractory epileptic patients suffer from uncontrolled seizures, their quality of life (QoL) may be improved if the seizure can be predicted in advance. In the preictal period, the excessive neuronal activity of epilepsy affects the autonomic nervous system. Since the fluctuation of the R-R interval (RRI) of an electrocardiogram (ECG), called heart rate variability (HRV), reflects the autonomic nervous function, an epileptic seizure may be predicted through monitoring RRI data. The present work proposes an HRV-based epileptic seizure monitoring method by utilizing multivariate statistical process control (MSPC) technology. Various HRV features are derived from the RRI data in both the interictal period and the preictal period recorded from epileptic patients, and an MSPC-based seizure prediction model is built from the interictal HRV features. The result of applying the proposed monitoring method to a clinical data demonstrates that seizures can be detected at least one minutes prior to the seizure onset. The possibility of realizing an HRV-based seizure monitoring system is shown.

Is prolongation of corrected QT interval associated with seizures induced by electroconvulsive therapy reduced by atropine sulfate?: SUZUKI et al.

Electrocardiogram abnormalities have been reported during electroconvulsive therapy (ECT). A corrected QT interval (QTc) prolongation indicates delayed ventricular repolarization, which can trigger ventricular arrhythmias such as torsade de pointes (TdP). We examined the QTc changes during generalized tonic‐clonic seizures induced by ECT, and the effects of atropine sulfate on these QTc changes.

Epileptic seizure monitoring by One-Class Support Vector Machine

Although refractory epileptic patients suffer from uncontrolled seizures, their quality of life (QoL) may be improved if the seizure can be predicted in advance. On the hypothesis that the excessive neuronal activity of epilepsy affects the autonomie nervous system and the fluctuation of the R-R interval (RRI) of an electrocardiogram (ECG), called heart rate variability (HRV), reflects the autonomie nervous function, there is possibility that an epileptic seizure can be predicted through monitoring RRI data. The present work proposes an HRV-based epileptic seizure monitoring method by utilizing One Class Support Vector Machine (OCSVM). Various HRV features are derived from the RRI data in both the interictal period and the preictal period, and an OCSVM-based seizure prediction model is built from the interictal HRV features. The application results of the proposed monitoring method to a clinical data are reported.

2-1-102. Changes in corrected QT interval during electroconvulsive therapy

Corrected QT interval (QTc) prolongation can trigger ventricular arrhythmia, including torsade de pointes (TdP). A QTc greater than 500xa0ms is considered to provide a high risk of TdP. Herein, we evaluated the risk of TdP during electroconvulsive therapy (ECT) using QTc. Twenty-two patients who underwent ECT were included. QTc were calculated with Hodges formula to avoid overcorrection of heart rate in a total of 201 ECT sessions. Baseline QTc was averaged over 30 s before stimulus onset. A QTc >457xa0ms was classified as a significant prolongation, and >500xa0ms as marked prolongation. The number of significant and marked QTc prolongations at baseline and the post-stimulus period were counted. At baseline, significant QTc prolongation was observed in 15 of 201 ECTs, while no patients showed marked prolongation. For post-stimulation, significant QTc prolongation and marked QTc prolongation was observed in 109 and 5 out of 201 ECT sessions, respectively. All the five marked QTc prolongations during post-ECT stimulation followed significant baseline QTc prolongation. These data suggest that a prolonged pre-stimulus QTc may be a risk factor of post-stimulus TdP in ECT.

ID 25 – Changes of cardiac autonomic nervous activity during a course of electroconvulsive therapy in depression

Objective Seizure duration has been reported to decrease across a course of electroconvulsive therapy (ECT) (anticonvulsant effect). Because dynamic autonomic activity changes have been described during ECT, and are affected by seizure generalization, we examined the relationship between longitudinal autonomic nervous activity changes on a course of ECT and seizure duration. Methods Electroencephalograms (EEG) and electrocardiograms (ECG) of twelve depressive patients were recorded during ECT procedures. The mean heart rate (HR) in 30xa0s prior to stimulus onset was defined as baseline HR. The T Max peak was designated as the data point with the maximum HR after stimulus onset. T 1/2 points were determined as the time point when HR was reduced midway between baseline HR and T Max HR. The changes of EEG seizure duration, T Max , and T 1/2 throughout the course of ECT, and their correlations, were examined. Results T 1/2 significantly decreased with repetition of ECT. T 1/2 was positively correlated to EEG seizure duration. Conclusions The time to return to the baseline from sympathetic nervous activation caused by ECT stimulation is shortened during a course of ECT. Key message Sympathetic nervous activity suppression and an anticonvulsant effect may contribute to the clinical action of ECT.