Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods: A hospital-based validation study.

OBJECTIVE: Automated seizure detection of focal epileptic seizures is needed for objective seizure quantification to optimize the treatment of patients with epilepsy. Heart rate variability (HRV)-based seizure detection using patient-adaptive threshold with logistic regression machine learning (LRML) methods has presented promising performance in a study with a Danish patient cohort. The objective of this study was to assess the generalizability of the novel LRML seizure detection algorithm by validating it in a dataset recorded from long-term video-EEG monitoring (LTM) in a Brazilian patient cohort. METHODS: Ictal and inter-ictal ECG-data epochs recorded during LTM were analyzed retrospectively. Thirty-four patients had 107 seizures (79 focal, 28 generalized tonic-clonic [GTC] including focal-to-bilateral-tonic-clonic seizures) eligible for analysis, with a total of 185.5 h recording. Because HRV-based seizure detection is only suitable in patients with marked ictal autonomic change, patients with >50 beats/min change in heart rate during seizures were selected as responders. The patient-adaptive LRML seizure detection algorithm was applied to all elected ECG data, and results were computed separately for responders and non-responders. RESULTS: The patient-adaptive LRML seizure detection algorithm yielded a sensitivity of 84.8% (95% CI: 75.6-93.9) with a false alarm rate of .25/24 h in the responder group (22 patients, 59 seizures). Twenty-five of the 26 GTC seizures were detected (96.2%), and 25 of the 33 focal seizures without bilateral convulsions were detected (75.8%). SIGNIFICANCE: The study confirms in a new, independent external dataset the good performance of seizure detection from a previous study and suggests that the method is generalizable. This method seems useful for detecting both generalized and focal epileptic seizures. The algorithm can be embedded in a wearable seizure detection system to alert patients and caregivers of seizures and generate objective seizure counts helping to optimize the treatment of the patients.

Automated detection of focal seizures using subcutaneously implanted electrocardiographic device: A proof-of-concept study.

Phase 2 studies showed that focal seizures could be detected by algorithms using heart rate variability (HRV) in patients with marked autonomic ictal changes. However, wearable surface electrocardiographic (ECG) devices use electrode patches that need to be changed often and may cause skin irritation. We report the first study of automated seizure detection using a subcutaneously implantable cardiac monitor (ICM; Confirm Rx, Abbott). For this proof-of-concept (phase 1) study, we recruited six patients admitted to long-term video-electroencephalographic monitoring. Fifteen-minute epochs of ECG signals were saved for each seizure and for control (nonseizure) epochs in the epilepsy monitoring unit (EMU) and in the patients' home environment (1-8 months). We analyzed the ICM signals offline, using a previously developed HRV algorithm. Thirteen seizures were recorded in the EMU, and 41 seizures were recorded in the home-monitoring period. The algorithm accurately identified 50 of 54 focal seizures (sensitivity = 92.6%, 95% confidence interval [CI] = 85.6%-99.6%). Twelve of the 13 seizures in the EMU were detected (sensitivity = 92.3%, 95% CI = 77.2%-100%), and 38 of the 41 seizures in the out-of-hospital setting were detected (sensitivity = 92.7%, 95% CI = 84.7%-100%). Four false detections were found in the 141 control (nonseizure) epochs (false alarm rate = 2.7/24 h). Our results suggest that automated seizure detection using a long-term, subcutaneous ICM device is feasible and accurate in patients with focal seizures and autonomic ictal changes.

Personalized seizure detection using logistic regression machine learning based on wearable ECG-monitoring device.

PURPOSE: Wearable automated detection devices of focal epileptic seizures are needed to alert patients and caregivers and to optimize the medical treatment. Heart rate variability (HRV)-based seizure detection devices have presented good detection sensitivity. However, false alarm rates (FAR) are too high. METHODS: In this phase-2 study we pursued to decrease the FAR, by using patient-adaptive logistic regression machine learning (LRML) to improve the performance of a previously published HRV-based seizure detection algorithm. ECG-data were prospectively collected using a dedicated wearable electrocardiogram-device during long-term video-EEG monitoring. Sixty-two patients had 174 seizures during 4,614 h recording. The dataset was divided into training-, cross-validation-, and test-sets (chronological) in order to avoid overfitting. Patients with >50 beats/min change in heart rate during first recorded seizure were selected as responders. We compared 18 LRML-settings to find the optimal algorithm. RESULTS: The patient-adaptive LRML-classifier in combination with using only responders to train the initial decision boundary was superior to both the generic approach and including non-responders to train the LRML-classifier. Using the optimal setting of the LRML in responders in the test dataset yielded a sensitivity of 78.2% and FAR of 0.62/24 h. The FAR was reduced by 31% compared to the previous method, upholding similar sensitivity. CONCLUSION: The novel, patient-adaptive LRML seizure detection algorithm outperformed both the generic approach and the previously published patient-tailored method. The proposed method can be implemented in a wearable online HRV-based seizure detection system alerting patients and caregivers of seizures and improve seizure-count which may help optimizing the patient treatment.

Common patterns of EEG reactivity in post-anoxic coma identified by quantitative analyses.

OBJECTIVE: Description of typical kinds of EEG reactivity (EEG-R) in post-anoxic coma using a quantitative method. METHODS: Study of 101 out-of-hospital cardiac arrest patients, 71 with good outcome (cerebral performance category scale ≤ 2). EEG was recorded 12-24 hours after cardiac arrest and four noxious, one auditory, and one visual stimulation were applied for 30 seconds each. Individual reference intervals for the power in the delta, theta, alpha, and beta bands were calculated based on six 2-second resting epochs just prior to stimulations. EEG-R in consecutive 2-second epochs after stimulation was expressed in Z-scores. RESULTS: EEG-R occurred roughly equally frequent as an increase or as a decrease in EEG activity. Sternal rub and sound stimulation were most provocative with the most pronounced changes as an increase in delta activity 4.5-8.5 seconds after stimulation and a decrease in theta activity 0.5-4.5 seconds after stimulation. These parameters predicted good outcome with an AUC of 0.852 (95 % CI: 0.771-0.932). CONCLUSIONS: Quantitative EEG-R is a feasible method for identification of common types of reactivity, for evaluation of stimulation methods, and for prognostication. SIGNIFICANCE: This method provides an objective measure of EEG-R revealing knowledge about the nature of EEG-R and its use as a diagnostic tool.

[Wearable devices for automated seizure detection].

The International League Against Epilepsy and the International Federation of Clinical Neurophysiology developed a clinical practice guideline on the use of automated seizure detection with wearable devices. They recommend using clinically validated devices for automated detection of generalized tonic-clonic seizures and focal to bilateral tonic-clonic seizures, especially in unsupervised patients, where alarms can result in rapid intervention. In this review, we investigate the published evidence behind the guideline, and we outline the need for future research.

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology.

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology.

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy, on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found moderate level of evidence for seizure types without GTCs or FBTCs. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

Biomarkers of seizure severity derived from wearable devices.

Besides triggering alarms, wearable seizure detection devices record a variety of biosignals that represent biomarkers of seizure severity. There is a need for automated seizure characterization, to identify high-risk seizures. Wearable devices can automatically identify seizure types with the highest associated morbidity and mortality (generalized tonic-clonic seizures), quantify their duration and frequency, and provide data on postictal position and immobility, autonomic changes derived from electrocardiography/heart rate variability, electrodermal activity, respiration, and oxygen saturation. In this review, we summarize how these biosignals reflect seizure severity, and how they can be monitored in the ambulatory outpatient setting using wearable devices. Multimodal recording of these biosignals will provide valuable information for individual risk assessment, as well as insights into the mechanisms and prevention of sudden unexpected death in epilepsy.

Peri-ictal heart rate variability parameters as surrogate markers of seizure severity.

This study aims at defining objective parameters reflecting the severity of peri-ictal autonomic changes and their relation to post-ictal generalized electroencephalography (EEG) suppression (PGES), with the view that such changes could be detected by wearable seizure detection systems and prove useful to assess the risk of sudden unexpected death in epilepsy (SUDEP). To this purpose, we assessed peri-ictal changes in heart rate variability (HRV) and correlated them with seizure duration, intensity of electromyography-based ictal muscle activity, and presence and duration of post-ictal generalized EEG suppression (PGES). We evaluated 75 motor seizures from 40 patients, including 61 generalized tonic-clonic seizures (GTCS) and 14 other major motor seizure types. For all major motor seizures, HRV measurements demonstrated a significantly decreased parasympathetic activity and increased sympathetic activity in the post-ictal period. The post-ictal increased sympathetic activity was significantly higher for GTCS as compared with non-GTCS. The degree of peri-ictal decreased parasympathetic activity and increased sympathetic activity was associated with longer PGES (>20 s), longer seizure duration, and greater intensity of ictal muscle activity. Mean post-ictal heart rate (HR) was an independent predictor of PGES duration, seizure duration, and intensity of ictal muscle contraction. Our results indicate that peri-ictal changes in HRV are potential biomarkers of major motor seizure severity.

Seizure detection using heart rate variability: A prospective validation study.

Although several validated seizure detection algorithms are available for convulsive seizures, detection of nonconvulsive seizures remains challenging. In this phase 2 study, we have validated a predefined seizure detection algorithm based on heart rate variability (HRV) using patient-specific cutoff values. The validation data set was independent from the previously published data set. Electrocardiography (ECG) was recorded using a wearable device (ePatch) in prospectively recruited patients. The diagnostic gold standard was inferred from video-EEG monitoring. Because HRV-based seizure detection is suitable only for patients with marked ictal autonomic changes, we defined responders as the patients who had a>50 beats/min ictal change in heart rate. Eleven of the 19 included patients with seizures (57.9%) fulfilled this criterion. In this group, the algorithm detected 20 of the 23 seizures (sensitivity: 87.0%). The algorithm detected all but one of the 10 recorded convulsive seizures and all of the 8 focal impaired awareness seizures, and it missed 2 of the 4 focal aware seizures. The median sensitivity per patient was 100% (in nine patients all seizures were detected). The false alarm rate was 0.9/24 h (0.22/night). Our results suggest that HRV-based seizure detection has high performance in patients with marked autonomic changes.

Seizure detection based on heart rate variability using a wearable electrocardiography device.

OBJECTIVE: To assess the feasibility and accuracy of seizure detection based on heart rate variability (HRV) using a wearable electrocardiography (ECG) device. Noninvasive devices for detection of convulsive seizures (generalized tonic-clonic and focal to bilateral tonic-clonic seizures) have been validated in phase 2 and 3 studies. However, detection of nonconvulsive seizures still needs further research, since currently available methods have either low sensitivity or an extremely high false alarm rate (FAR). METHODS: In this phase 2 study, we prospectively recruited patients admitted to long-term video-EEG monitoring (LTM). ECG was recorded using a dedicated wearable device. Seizures were automatically detected using HRV parameters computed off-line, blinded to all other data. We compared the performance of 26 automated algorithms with the seizure time-points marked by experts who reviewed the LTM recording. Patients were classified as responders if >66% of their seizures were detected. RESULTS: We recruited 100 consecutive patients and analyzed 126 seizures (108 nonconvulsive and 18 convulsive) from 43 patients who had seizures during monitoring. The best-performing HRV algorithm combined a measure of sympathetic activity with a measure of how quickly HR changes occurred. The algorithm identified 53.5% of the patients with seizures as responders. Among responders, detection sensitivity was 93.1% (95% CI: 86.6%-99.6%) for all seizures and 90.5% (95% CI: 77.4%-97.3%) for nonconvulsive seizures. FAR was 1.0/24 h (0.11/night). Median seizure detection latency was 30 s. Typically, patients with prominent autonomic nervous system changes were responders: An ictal change of >50 heartbeats per minute predicted who would be responder with a positive predictive value of 87% and a negative predictive value of 90%. SIGNIFICANCE: The automated HRV algorithm, using ECG recorded with a wearable device, has high sensitivity for detecting seizures, including the nonconvulsive ones. FAR was low during the night. This approach is feasible in patients with prominent ictal autonomic changes.

Non-electroencephalography-based seizure detection.

PURPOSE OF REVIEW: There is need for automated seizure detection using mobile or wearable devices, for objective seizure documentation and decreasing morbidity and mortality associated with seizures. Due to technological development, a high number of articles have addressed non-electroencephalography (EEG)-based seizure detection. However, the quality of study-design and reporting is extremely heterogeneous. We aimed at giving the reader a clear picture on the current state of seizure detection, describing the level of evidence behind the various devices. RECENT FINDINGS: Fifteen studies of phase-2 or above, demonstrated that non-EEG-based devices detected generalized tonic-clonic seizures (GTCS) with high sensitivity (≥90%) and low false alarm rate (FAR) (down to 0.2/day). We found limited evidence for detection of motor seizures other than GTCS, mostly from subgroups in larger studies, targeting GTCS. There is little evidence for non-EEG-based detection of nonmotor seizures: sensitivity is low (19-74%) with extremely high FAR (50-216/day). SUMMARY: Detection of GTCS is reliable and there are several, validated devices on the market. However, detection of other seizure types needs further research.

Observations on muscle activity in REM sleep behavior disorder assessed with a semi-automated scoring algorithm.

OBJECTIVES: Rapid eye movement (REM) sleep behavior disorder (RBD) is defined by dream enactment due to a failure of normal muscle atonia. Visual assessment of this muscle activity is time consuming and rater-dependent. METHODS: An EMG computer algorithm for scoring 'tonic', 'phasic' and 'any' submental muscle activity during REM sleep was evaluated compared with human visual ratings. Subsequently, 52 subjects were analyzed with the algorithm. Duration and maximal amplitude of muscle activity, and self-awareness of RBD symptoms were assessed. RESULTS: The computer algorithm showed high congruency with human ratings and all subjects with RBD were correctly identified by excess of submental muscle activity, when artifacts were removed before analysis. Subjects with RBD exhibited prolonged bouts of 'phasic' muscle activity with high amplitude. Self-awareness of RBD symptoms correlated with amount of REM sleep without atonia. CONCLUSIONS: Our proposed algorithm was able to detect and rate REM sleep without atonia allowing identification of RBD. Increased duration and amplitude of muscle activity bouts were characteristics of RBD. Quantification of REM sleep without atonia represents a marker of RBD severity. SIGNIFICANCE: Our EMG computer algorithm can support a diagnosis of RBD while the quantification of altered muscle activity provides a measure of its severity.

Evaluation of the noradrenergic system in Parkinson's disease: an 11C-MeNER PET and neuromelanin MRI study.

Pathological involvement of the noradrenergic locus coeruleus occurs early in Parkinson's disease, and widespread noradrenaline reductions are found at post-mortem. Rapid eye movement sleep behaviour disorder (RBD) accompanies Parkinson's disease and its presence predicts an unfavourable disease course with a higher propensity to cognitive impairment and orthostatic hypotension. MRI can detect neuromelanin in the locus coeruleus while 11C-MeNER PET is a marker of noradrenaline transporter availability. Here, we use both imaging modalities to study the association of RBD, cognition and autonomic dysfunction in Parkinson's disease with loss of noradrenergic function. Thirty non-demented Parkinson's disease patients [16 patients with RBD and 14 without RBD, comparable across age (66.6 ± 6.7 years), sex (22 males), and disease stage (Hoehn and Yahr, 2.3 ± 0.5)], had imaging of the locus coeruleus with neuromelanin sensitive MRI and brain noradrenaline transporter availability with 11C-MeNER PET. RBD was confirmed with polysomnography; cognitive function was assessed with a neuropsychological test battery, and blood pressure changes on tilting were documented; results were compared to 12 matched control subjects. We found that Parkinson's disease patients with RBD showed decreased locus coeruleus neuromelanin signal on MRI (P < 0.001) and widespread reduced binding of 11C-MeNER (P < 0.001), which correlated with amount of REM sleep without atonia. Parkinson's disease with RBD was also associated with a higher incidence of cognitive impairment, slowed EEG activity, and orthostatic hypotension. Reduced 11C-MeNER binding correlated with EEG slowing, cognitive performance, and orthostatic hypotension. In conclusion, reduced noradrenergic function in Parkinson's disease was linked to the presence of RBD and associated with cognitive deterioration and orthostatic hypotension. Noradrenergic impairment may contribute to the high prevalence of these non-motor symptoms in Parkinson's disease, and may be of relevance when treating these conditions in Parkinson's disease.

Comparing maximum autonomic activity of psychogenic non-epileptic seizures and epileptic seizures using heart rate variability.

PURPOSE: The semiology of psychogenic non-epileptic seizures (PNES) can resemble epileptic seizures, and differentiation between epileptic seizures with no EEG-correlate and PNES can be challenging even for trained experts. Therefore, there has been a search for a quantitative measure, other than EEG and semiology that could distinguish PNES from epileptic seizures. We used ECG to measure heart rate variability (HRV) in order to compare maximum autonomic activity of epileptic seizures and PNES. These comparisons could potentially serve as biomarkers for distinguishing these types of clinical episodes. METHOD: Forty-nine epileptic seizures from 17 patients and 24 PNES from 7 patients with analyzable ECG were recorded during long-term video-EEG monitoring. Moving windows of 100 R-R intervals throughout each seizure were used to find maximum values of Cardiac Sympathetic Index (CSI) (sympathetic tonus) and minimum values of Cardiac Vagal Index (CVI), Root-Mean-Square-of-Successive-Differences (RMSSD) and HF-power (parasympathetic tonus). In addition, non-seizure recordings of each patient were used to compare HRV-parameters between the groups. RESULTS: The maximum CSI for epilepsy seizures were higher than PNES (P=0.015). The minimum CVI, minimum RMSSD and HF-power did not show significant difference between epileptic seizures and PNES (P=0.762; P=0.152; P=0.818). There were no statistical difference of non-seizure HRV-parameters between the PNES and epilepsy patients. CONCLUSION: We found the maximum sympathetic activity accompanying the epileptic seizures to be higher, than that during the PNES. However, the great variation of autonomic response within both groups makes it difficult to use these HRV-measures as a sole measurement in distinguishing epileptic seizures from PNES.

Exploring the capability of wireless near infrared spectroscopy as a portable seizure detection device for epilepsy patients.

PURPOSE: Near infrared spectroscopy (NIRS) has proved useful in measuring significant hemodynamic changes in the brain during epileptic seizures. The advance of NIRS-technology into wireless and portable devices raises the possibility of using the NIRS-technology for portable seizure detection. METHODS: This study used NIRS to measure changes in oxygenated (HbO), deoxygenated (HbR), and total hemoglobin (HbT) at left and right side of the frontal lobe in 33 patients with epilepsy undergoing long-term video-EEG monitoring. Fifteen patients had 34 focal seizures (20 temporal-, 11 frontal-, 2 parietal-lobe, one unspecific) recorded and analyzed with NIRS. Twelve parameters consisting of maximum increase and decrease changes of HbO, HbR and HbT during seizures (1 min before- to 3 min after seizure-onset) for left and right side, were compared with the patients' own non-seizure periods (a 2-h period and a 30-min exercise-period). In both non-seizure periods 4 min moving windows with maximum overlapping were applied to find non-seizure maxima of the 12 parameters. Detection was defined as positive when seizure maximum change exceeded non-seizure maximum change. RESULTS: When analyzing the 12 parameters separately the positive seizure detection was in the range of 6-24%. The increase in hemodynamics was in general better at detecting seizures (15-24%) than the decrease in hemodynamics (6-18%) (P=0.02). CONCLUSION: NIRS did not seem to be a suitable technology for generic seizure detection given the device, settings, and methods used in this study. There are still several challenges to overcome before the NIRS-technology can be used as a home-monitoring seizure detection device.

Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot.

PURPOSE: In order to assess whether focal epileptic seizures can be detected and distinguished from exercise we evaluated four different heart rate variability (HRV) methods with short term moving window analysis of 30, 50 or 100 R-R intervals or seconds per analyzed window. METHODS: The four methods consisted of: (1) reciprocal high frequency power based on Fast Fourier Transformation, (2) Cardiac Sympathetic Index (CSI), (3) Modified CSI both based on Lorenz plot, and (4) heart rate differential method. Seventeen patients (12 males, 5 females; age 20-55) had 47 seizures (including three secondary generalized tonic-clonic (sGTC)), which were analyzed during their long term video-EEG monitoring of 1-5 days duration. Positive seizure detection was regarded, when the HRV-value during seizures (1min before to 3min after seizure-onset) exceeded 105% of the highest value during exercise and non-seizure sample-periods of the same patient. RESULTS: Modified CSI100 was the most accurate method: it detected all seizures for 13 of the 17 patients within 6s before till 50s after seizure onset time, even though exercise maximum HR of each patient exceeded that of the seizures. The three sGTC seizures were all detected more than half a minute before the tonic-clonic phase. CONCLUSION: The results indicate a detectable, sudden and inordinate shift toward sympathetic overdrive in the sympathovagal balance of the autonomic nervous system around seizure-onset time, for most patients. The Modified CSI is a promising parameter for a portable ECG-based epilepsy alarm, detecting both focal and sGTC seizures.

Heart rate variability analysis indicates preictal parasympathetic overdrive preceding seizure-induced cardiac dysrhythmias leading to sudden unexpected death in a patient with epilepsy.

Evidence for seizure-induced cardiac dysrhythmia leading to sudden unexpected death in epilepsy (SUDEP) has been elusive. We present a patient with focal cortical dysplasia who has had epilepsy for 19 years and was undergoing presurgical evaluation. The patient did not have any cardiologic antecedents. During long-term video-electroencephalography (EEG) monitoring, following a cluster of secondarily generalized tonic-clonic seizures (GTCS), the patient had prolonged postictal generalized EEG suppression, asystole, followed by arrhythmia, and the patient died despite cardiopulmonary resuscitation. Analysis of heart rate variability showed a marked increase in the parasympathetic activity during the period preceding the fatal seizures, compared with values measured 1 day and 7 months before, and also higher than the preictal values in a group of 10 patients with GTCS without SUDEP. The duration of the QTc interval was short (335-358 msec). This unfortunate case documented during video-EEG monitoring indicates that autonomic imbalance and seizure-induced cardiac dysrhythmias contribute to the pathomechanisms leading to SUDEP in patients at risk (short QT interval).

Using Lorenz plot and Cardiac Sympathetic Index of heart rate variability for detecting seizures for patients with epilepsy.

Tachycardia is often seen during epileptic seizures, but it also occurs during physical exercise. In order to assess whether focal epileptic seizures can be detected by short term moving window Heart Rate Variability (HRV) analysis, we modified the geometric HRV method, Lorenz plot, to consist of only 30, 50 or 100 R-R intervals per analyzed window. From each window we calculated the longitudinal (L) and transverse (T) variability of Lorenz plot to retrieve the Cardiac Sympathetic Index (CSI) as (L/T) and "Modified CSI" (described in methods), and compared the maximum during the patient's epileptic seizures with that during the patient's own exercise and non-seizure sessions as control. All five analyzed patients had complex partial seizures (CPS) originating in the temporal lobe (11 seizures) during their 1-5 days long term video-EEG monitoring. All CPS with electroencephalographic correlation were selected for the HRV analysis. The CSI and Modified CSI were correspondently calculated after each heart beat depicting the prior 30, 50 and 100 R-R intervals at the time. CSI (30, 50 and 100) and Modified CSI (100) showed a higher maximum peak during seizures than exercise/non-seizure (121-296%) for 4 of the 5 patients within 4 seconds before till 60 seconds after seizure onset time even though exercise maximum HR exceeded that of the seizures. The results indicate a detectable, sudden and inordinate shift towards sympathetic overdrive in the sympathovagal balance of the autonomic nervous system just around seizure-onset for certain patients. This new modified moving window Lorenz plot method seems promising way of constructing a portable ECG-based epilepsy alarm for certain patients with epilepsy who needs aid during seizure.