Epileptic seizure prediction based on features extracted from lagged Poincaré plots.

OBJECTIVE: The present work proposes a new epileptic seizure prediction method based on lagged Poincaré plot analysis of heart rate (HR). METHODS: In this article, the Poincaré plots with six different lags (1-6) were constructed for four episodes of heart rate variability (HRV) before the seizures. Moreover, two features were extracted based on lagged Poincare plots, which include the angle between the time series and the ellipse density fitted to the RR points. RESULTS: The proposed method was applied to 16 epileptic patients with 170 seizures. The results included sensitivity of 80.42% for the angle feature and 75.19% for the density feature. The false-positive rate was 0.15/Hr, which indicates that the system has superiority over the random predictor. CONCLUSION: The proposed HRV-based epileptic seizure prediction method has the potential to be used in daily life because HR can be measured easily by using a wearable sensor.

New criteria for evaluation of electroretinogram in patients with retinitis pigmentosa.

BACKGROUND: Electroretinogram (ERG) plays an essential role in the diagnosis of retinal disease. Choosing appropriate methods could extract valuable information from ERG. In this study, a new criterion based on time-frequency domain analysis was proposed to investigate the retina in retinitis pigmentosa (RP) patients. MATERIALS AND METHODS: The total number of 16 eyes from eight RP patients and 20 eyes from age-matched healthy subjects were assessed. The signals included photopic and scotopic ERGs. Continuous wavelet transform was applied to ERGs. Dominant frequencies were extracted, and the contours related to these dominant frequencies were selected. As a new criterion, the areas related to dominant frequency contours were considered a feature to differentiate the RP and normal groups. To better evaluate the proposed criterion results, the time-domain analysis characteristics of ERG were also considered. RESULTS: The results showed an increase in implicit time and reduced amplitude in RP patients (P < 0.05). A significant decrease of dominant frequencies and increasing their occurrence time were seen in ERG of RP patients. Also, in RP patients, the third dominant frequency was disappeared from the three main frequencies observed in photopic ERGs of normal subjects. The area criterion showed a significant decrease in RP groups (P < 0.05). CONCLUSION: RP can cause changes in the time and time-frequency components of the ERG. The area index could represent a new view of the characteristics of the ERG in the time-frequency domain. This criterion can help the ophthalmologist to have a better evaluation of retinal disease.

Continuous wavelet transform analysis of ERG in patients with diabetic retinopathy.

PURPOSE: Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide. Non-proliferative diabetic retinopathy (NPDR) is a stage of the disease that contains morphological and functional disruption of the retinal vasculature and dysfunction of retinal neurons. This study aimed to compare time and time-frequency-domain analysis in the evaluation of electroretinograms (ERGs) in subjects with NPDR. METHOD: The ERG responses were recorded in 16 eyes from 12 patients with NPDR and 24 eyes from 12 healthy subjects as the control group. The implicit time, amplitude, and time-frequency-domain parameters of photopic and scotopic ERGs were analyzed. RESULTS: The implicit times of b-waves in the dark-adapted 10.0 (P = 0.0513) and light-adapted 3.0 (P = 0.0414) were significantly increased in the NPDR group. The amplitudes of a- and b-wave showed a significantly decreased dark-adapted 10.0 (P = 0.0212; P = 0.0133) and light-adapted 3.0 (P = 0.0517; P = 0.0021) ERG of the NPDR group. The Cohen's d effect size had higher values in the amplitude of dark-adapted 10.0 b-wave (|d|= 1.8058) and amplitude of light-adapted 3.0 b-wave (|d|= 1.9662). The CWT results showed that the frequency ranges of the dominant components in dark-adapted 10.0 and light-adapted 3.0 ERG were decreased in the NPDR group compared to the healthy group (P < 0.05). The times associated with the NDPR group's dominant components were increased compared to normal eyes in both dark-adapted 10.0 and light-adapted 3.0 ERG (P < 0.05). All Cohen's d effect sizes of the implicit times and dominant frequency components were on a large scale (|d|> 1). CONCLUSION: These findings suggest that the time and time-frequency parameters of both photopic and scotopic ERGs can be good indicators for DR. However, time-frequency-domain analysis could present more information might be helpful in the assessment of the DR severity.

A novel method for electroretinogram assessment in patients with central retinal vein occlusion.

PURPOSE: Central retinal vein occlusion (CRVO) is the second most common retinal vascular disorder after diabetic retinopathy that affects the eyes. We propose a method for distinction of normal and central CRVO eyes based on electroretinogram (ERG). METHODS: Seventeen patients with CRVO in one eye were analyzed. Their ERG signals were collected in six different stimuli, including four records in the darkness (dark-adapted 0.01, dark-adapted 3.0, dark-adapted oscillatory potentials, and dark-adapted 10) and two records in brightness (light-adapted 3.0 and light-adapted 30 Hz flicker). Nonlinear features such as Hurst exponent (HE) and approximate entropy (ApEn) were extracted from healthy and CRVO eyes. Finally, a parabolic mapping and two criteria (theta angle and the density of points) were proposed to distinguish the groups. RESULTS: For ApEn, the P values of dark-adapted 3.0 oscillatory (P = 0.0433) and flicker (P = 0.0425) confirmed significant differences between the groups. For HE, the P values of dark-adapted 3.0 oscillatory (P = 0.0421) and flicker 30 Hz (P = 0.0402) confirmed differences between the healthy and CRVO groups. The P values of theta angle for dark-adapted 3.0 (P = 0.0199), dark-adapted oscillatory (P = 0.0265), dark-adapted 10.0 (P = 0.0166), light-adapted 3.0 (P = 0.0411), and flicker (P = 0.0361) showed significant differences. Using the density criterion, the statistical test demonstrated a significant difference between the groups in dark-adapted 3 (P = 0.0038), dark-adapted oscillatory (P = 0.0102), dark-adapted 10.0 (P = 0.0071), light-adapted 3.0 (P = 0.0319), and flicker 30 Hz (P = 0.0076). CONCLUSION: The proposed features have made it possible to distinguish between healthy and CRVO eyes. This method could be helpful in some cases with no definite diagnosis or to estimate the severity of CRVO.

Application of a Mapping Method in the Analysis of Electroretinogram in Patients with Retinitis Pigmentosa.

PURPOSE: Retinitis pigmentosa (RP) is a group of degenerative retinal diseases characterized by mutations in genes affecting retinal pigment epithelium (RPE) function, as well as mutations directly involving photoreceptors. This paper aims to evaluate a nonlinear method to distinguish between the RP and normal eye based on the Electroretinogram (ERG) signal. METHOD: ERG signal was recorded from 28 eyes of patients with the RP and 32 normal eyes. The ERG signal consists of four different stimuli, including two dark-adapted and two light-adapted stimuli. The time-domain analysis includes the amplitude and implicit time to consider the robustness of the nonlinear method. A parabolic mapping method was performed, and two criteria (Theta angle and density) extracted from the parabola were compared for both groups. RESULTS: The results showed that a-wave's amplitude and implicit time significantly changed in the dark- and light-adapted stimuli. The amplitude of the b-wave showed significant changes in all stimuli. However, the implicit time of b-wave had a significant increase only in the dark-adapted 3.0 ERG. Both nonlinear criteria showed significant changes in the RP group for all the stimuli. The p-values of dark-adapted 3.0 (p = .0121), dark-adapted 10.0 (p = .0014), light-adapted 3.0 (p = .0119), and flicker 30 Hz (p = .0323) showed significant differences. Using the density criterion, the statistical test demonstrated a significant difference between the RP and healthy normal group in dark-adapted 3.0 (p = .0076), dark-adapted 10.0 (p = .0024), light-adapted 3.0 (p = .0021), and flicker 30 Hz (p = .0165). CONCLUSION: The proposed features have made it possible to distinguish between healthy and RP eyes. This method might be helpful in early diagnosis.

Electrocardiogram and heart rate variability assessment in patients with common autoimmune diseases: a methodological review.

The aim of this article was to summarize current knowledge about the potential clinical utility of electrocardiogram (ECG) and heart rate variability (HRV) measures in patients with 4 common autoimmune diseases (ADs): rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), Behcet's disease (BD), and systemic sclerosis (SSc). A search was conducted of the PubMed, Embase, and Scopus databases using terms and a controlled vocabulary associated with these ADs, ECG, and HRV. The available, full-text articles published in English were considered. In all, 20 publications that examined the direct effect of these diseases on the heart were selected according to a systematic review protocol. Time-frequency domain analysis revealed that HRV parameters were lower in patients with the selected ADs in comparison with control groups. An increased QT dispersion and heart rate corrected QT, which are well-known as risk factors for sudden cardiac death, were observed in the patient group. In some studies, a correlation was seen between the duration of the disease and its activity, while others did not report such an association. Heart rate turbulence parameters were also examined. Turbulence onset was increased in SLE and SSc patients, while the turbulence slope was decreased in SLE patients. There was no significant change in these parameters in BD patients. Patients with ADs demonstrate abnormal HRV and ECG parameters, which indicates an autonomic cardiac functional impairment. Measurement of these parameters can be a useful clinical tool in the diagnosis and prediction of some disorders in patients with ADs. Both of these signals can provide helpful information for physicians to trace the efficacy of prescribed medicines.

Time-Frequency Analysis of Photopic Negative Response in CRVO Patients.

PURPOSE: The PhNR is driven by retinal ganglion cells (RGCs). Therefore, the function of RGCs could be objectively evaluated by analyzing the PhNR. The aim of this article is to determine the effect of central retinal vein occlusion (CRVO) on PhNR and RGCs performances. METHODS: Seventeen patients with CRVO were included. Full-field photopic ERGs, including PhNR, were recorded and compared with the fellow normal eyes. ERG signals were analyzed based on the standard time-domain analyses of the PhNR as well as a continuous wavelet transform (CWT) to extract time-frequency components that correspond to the PhNR using MATLAB. We obtained the main frequencies and their occurrence time from CWT. RESULTS: All a-wave, b-wave, and PhNR amplitudes of CRVO eyes showed a significant reduction compared to those of the fellow eyes (P < .01, P < .001, and P < .001, respectively). The peak times of a-wave, b-wave, and PhNR were increased significantly in the CRVO eyes (P = .04, P = .04, and P = .003, respectively). The dominant f3 frequency, which corresponds to the PhNR in CRVO patients, showed a more significant decrease (P < .001) compared to other dominant frequencies (f0, f1, and f2). The occurrence time of f3 (t3) was significantly higher in the CRVO eyes (P < .001). Time-domain of the PhNR was also affected in CRVO patients (P < .001). CONCLUSION: CWT allows quantifications of ERG responses, especially for PhNR. The PhNR was severely affected in CRVO eyes implicating loss of RGCs. CWT might demonstrate the severity of CRVO more precisely and identify diagnostically significant changes of ERG waveforms that are not resolved when the analysis is only limited to the time-domain measurements.

A hybrid algorithm for heart sounds segmentation based on phonocardiogram.

For many years, heart function has been measured by the electrocardiogram (ECG) signal, while sounds produced in the heart can also contain information indicating normal or abnormal heart function. What has caused to restrict the use of the phonocardiography (PCG) signal was the lack of mastery of experts in the interpretation of these sounds, as well as its high potential for noise pollution. PCG is a non-invasive signal for monitoring physiological parameters of cardiac, which can make heart disease diagnostics more efficient. In recent years, attempts have been made to use PCG to detect heart disease independently without a need to match with the ECG. We propose a hybrid algorithm including empirical mode decomposition (EMD), Hilbert transform and Gaussian function for detecting heart sounds to distinguish first (S1) and second (S2) cardiac sounds by eliminating the effect of cardiac murmurs. In this article, 250 normal and 250 abnormal sound signals were examined. The overall positive predictivity of normal and abnormal S1 and S2 is 98.98%, 98.78, 98.78 and 98.37, respectively. Our results showed that the proposed method has a high potential for heart sounds determination, while maintains its simplicity and has a reasonable computational time.

A review of significant research on epileptic seizure detection and prediction using heart rate variability.

Epilepsy is a brain disorder that many people struggle with all over the world. Despite extensive research, epilepsy is still an important challenge without a clear solution. There may be confusion about providing a specific approach due to the variety of epileptic seizures and the effectiveness in different environmental conditions. Some patients with epilepsy undergo treatment through medication or surgery. Epileptic patients suffer from unpredictable conditions that may occur at any moment. Given the origins of these seizures, researchers have focused on predicting epileptic seizures via electroencephalogram (EEG). The results indicate some success in this regard. This success led to a focus on optimizing these methods and the evaluation of epilepsy seizure prediction through other vital signals. Both sympathetic and parasympathetic inhibitory effects are undeniable during epileptic seizures. This conflict is visible in the change in heart rate. In recent years several investigations have focused on a behavioral study of heart rate changes before the seizures. The results have led to the development of algorithms for classifying and predicting epileptic seizures using the electrocardiogram (ECG) and the more distinct heart rate variability (HRV). This article presents an overview of seizure detection and prediction methods and discusses their potential to improve the quality of life of epileptic patients.

Gender-Related Differences in Heart Rate Variability of Epileptic Patients.

Recently, heart rate variability (HRV) analysis has been used as an indicator of epileptic seizures. As women have a lower sudden, unexpected death in epilepsy risk and greater longevity than men, the authors postulated that there are significant gender-related differences in heart rate dynamics of epileptic patients. The authors analyzed HRV during 5-minute segments of continuous electrocardiogram recording of age-matched populations. The middle-aged epileptic patients included males ( n = 12) and females ( n = 12), ranging from 41 to 65 years of age. Relatively high- (0.15 Hz-0.40 Hz) and low-frequency (0.01 Hz-0.15 Hz) components of HRV were computed using spectral analysis. Poincaré parameters of each heart rate time series were considered as nonlinear features. The mean heart rate markedly differed between gender groups including both right- and left-sided seizures. High-frequency heart rate power and the low-frequency/high-frequency ratio increased in the pre-ictal phase of both male and female groups ( p < .01), but men showed more increase especially in right-sided seizures. The standard deviation ratio, SD2/ SD1, of pre-ictal phase was greater in males than females ( p < .01). High-frequency spectral power and parasympathetic activity were higher in the female group with both right- and left-sided seizures. Men showed a sudden increase in sympathetic activity in the pre-ictal phase, which might increase the risk of cardiovascular disease in comparison to women. These complementary findings indicate the need to account for gender, as well as localization in HRV analysis.

Classification of ictal and seizure-free HRV signals with focus on lateralization of epilepsy.

OBJECTIVE: Epileptic onsets often affect the autonomic function of the body during a seizure, whether it is in ictal, interictal or post-ictal periods. The different effects of localization and lateralization of seizures on heart rate variability (HRV) emphasize the importance of autonomic function changes in epileptic patients. On the other hand, the detection of seizures is of primary interests in evaluating the epileptic patients. In the current paper, we analyzed the HRV signal to develop a reliable offline seizure-detection algorithm to focus on the effects of lateralization on HRV. MATERIALS AND METHODS: We assessed the HRV during 5-min segments of continuous electrocardiogram (ECG) recording with a total number of 170 seizures occurred in 16 patients, composed of 86 left-sided and 84 right-sided focus seizures. Relatively high and low-frequency components of the HRV were computed using spectral analysis. Poincaré parameters of each heart rate time series considered as non-linear features. We fed these features to the Support Vector Machines (SVMs) to find a robust classification method to classify epileptic and non-epileptic signals. Leave One Out Cross-Validation (LOOCV) approach was used to demonstrate the consistency of the classification results. RESULTS: Our obtained classification accuracy confirms that the proposed scheme has a potential in classifying HRV signals to epileptic and non-epileptic classes. The accuracy rates for right-sided and left-sided focus seizures were obtained as 86.74% and 79.41%, respectively. CONCLUSIONS: The main finding of our study is that the patients with right-sided focus epilepsy showed more reduction in parasympathetic activity and more increase in sympathetic activity. It can be a marker of impaired vagal activity associated with increased cardiovascular risk and arrhythmias. Our results suggest that lateralization of the seizure onset zone could exert different influences on heart rate changes. A right-sided seizure would cause an ictal tachycardia whereas a left-sided seizure would result in an ictal bradycardia.

Prediction of epileptic seizures based on heart rate variability.

BACKGROUND: Until now, different approaches have been published to resolve the problem of predicting epileptic seizures. The results are reminiscent of a substantial need for improvements in these methods to reach the stage of the clinical application. Our aim is to develop a reliable epileptic seizure prediction algorithm based on the Heart Rate Variability (HRV) analysis. METHODS: We analyzed the HRV of sixteen epileptic patients with a total of 170 seizures, to predict the occurrence of seizures based on the dynamic changes of Electrocardiogram (ECG) during the pre-ictal period. Time and frequency-domain features were computed forthe consecutive time windows with a length of five minutes. An adaptive decision threshold method was used for raising alarms. Predictions were made when selected features exceeded the decision thresholds. RESULTS: For the seizure occurrence period (SOP) of 4:30 minutes, and intervention time (IT) of 110 Sec, the presented method showed an average sensitivity of 78.59%, and average false prediction rate of 0.21/Hr, which indicates that the system has superiority to the random predictor. CONCLUSION: The proposed approach shows a potential in the monitoring of epileptic patients and improving their life quality. The overall performance of the algorithm is a step forward for clinical implementation.

Pre-ictal heart rate variability assessment of epileptic seizures by means of linear and non-linear analyses.

OBJECTIVE: The purpose of the present study was to analyze the effects of epilepsy on the autonomic control of the heart in pre-ictal phase in order to find an algorithm of early detection of seizure onset. METHODS: Overall 133 epileptic seizures were analyzed from 12 patients with epilepsy (seven males and five females; mean age 43.91 years, SD: 10.16) participated in this study. Single lead electrocardiogram recordings of epileptic patients were compiled. 240, 90-30, 30-10 and 5 minutes heart rate variability (HRV) signals of preseizure were chosen for analysis of heart rate. As HRV signals are non-stationary, a set of time and frequency domain features (Mean HR, Triangular Index, LF, HF, LF/HF) and nonlinear parameters (SD1, SD2 and SD2/SD1 indices derived from Poincare plots) extracted from HRV is analyzed. Statistical analysis was performed using paired sample t-test for comparisons of the segments and differences between pre-ictal segments were evaluated by Tukey tests. RESULTS: There was slight tachycardia in segments near the seizure (30 minutes before: 85.3517 bpm, 5 minutes before: 119.3630.82 bpm, p=0.0207) which significantly differ from baseline in segments far from seizure (240 minutes before: 66.5211.7 bpm). Also there was significant increase in LF/HF ratio (30 minutes before: 1.10.22, 5 minutes before: 2.120.5, p=0.0332) and SD2/SD1 ratio (30 minutes before: 1.20.15, 5 minutes before: 2.030.55, p=0.0431) when compared to segments far from the seizure (240 minutes before: 0.780.24 and 0.780.14) respectively. Although there was about decrease of triangular index in segments near the seizure the percentage of decrease was not comparable to segments far from the seizure. CONCLUSION: Significant changes of HRV parameters in pre-ictal (5 minutes before the seizure) are obviously higher in comparison to interictal baseline. Pre-ictal significant changes of HRV suggesting that this time can be considered as prediction time for designing an algorithm of early detection of seizure onset based on HRV.