Reza Tafreshi

Automated Real-Time Epileptic Seizure Detection in Scalp EEG Recordings Using an Algorithm Based on Wavelet Packet Transform

A novel wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG is proposed. In a moving-window analysis, the EEG from each channel is decomposed by wavelet packet transform. Using wavelet coefficients from seizure and nonseizure references, a patient-specific measure is developed to quantify the separation between seizure and nonseizure states for the frequency range of 1-30 Hz. Utilizing this measure, a frequency band representing the maximum separation between the two states is determined and employed to develop a normalized index, called combined seizure index (CSI). CSI is derived for each epoch of every EEG channel based on both rhythmicity and relative energy of that epoch as well as consistency among different channels. Increasing significantly during ictal states, CSI is inspected using one-sided cumulative sum test to generate proper channel alarms. Analyzing alarms from all channels, a seizure alarm is finally generated. The algorithm was tested on scalp EEG recordings from 14 patients, totaling 75.8 h with 63 seizures. Results revealed a high sensitivity of 90.5% , a false detection rate of 0.51 h-1 and a median detection delay of 7 s. The algorithm could also lateralize the focus side for patients with temporal lobe epilepsy.

Predicting Epileptic Seizures in Scalp EEG Based on a Variational Bayesian Gaussian Mixture Model of Zero-Crossing Intervals

A novel patient-specific seizure prediction method based on the analysis of positive zero-crossing intervals in scalp electroencephalogram (EEG) is proposed. In a moving-window analysis, the histogram of these intervals for the current EEG epoch is computed, and the values corresponding to specific bins are selected as an observation. Then, the set of observations from the last 5 min is compared with two reference sets of data points (preictal and interictal) through novel measures of similarity and dissimilarity based on a variational Bayesian Gaussian mixture model of the data. A combined index is then computed and compared with a patient-specific threshold, resulting in a cumulative measure which is utilized to form an alarm sequence for each channel. Finally, this channel-based information is used to generate a seizure prediction alarm. The proposed method was evaluated using ~ 561 h of scalp EEG including a total of 86 seizures in 20 patients. A high sensitivity of 88.34% was achieved with a false prediction rate of 0.155 h-1 and an average prediction time of 22.5 min for the test dataset. The proposed method was also tested against a Poisson-based random predictor.

An entropy-based approach to predict seizures in temporal lobe epilepsy using scalp EEG

We describe a novel algorithm for the prediction of epileptic seizures using scalp EEG. The method is based on the analysis of the positive zero-crossing interval series of the EEG signal and its first and second derivatives as a measure of brain dynamics. In a moving-window analysis, we estimated the probability density of these intervals and computed the differential entropy. The resultant entropy time series were then inspected using the cumulative sum (CUSUM) procedure to detect decreases as precursors of upcoming seizures. In the next step, the alarm sequences resulting from analysis of the EEG waveform and its derivatives were combined. Finally, a seizure prediction index was generated based on the spatio-temporal processing of the combined CUSUM alarms. We evaluated our algorithm using a dataset of ∼21.5 hours of multichannel scalp EEG recordings from four patients with temporal lobe epilepsy, resulting in 87.5% sensitivity, a false prediction rate of 0.28/hr, and an average prediction time of 25 min.

Second-Order Sliding Mode Strategy for Air–Fuel Ratio Control of Lean-Burn SI Engines

Higher fuel economy and lower exhaust emissions for spark-ignition engines depend significantly on precise air-fuel ratio (AFR) control. However, the presence of large time-varying delay due to the additional modules integrated with the catalyst in the lean-burn engines is the primary limiting factor in the control of AFR. In this paper, the engine dynamics are rendered into a nonminimum phase system using Padé approximation. A novel systematic approach is presented to design a parameter-varying dynamic sliding manifold to compensate for the instability of the internal dynamics while achieving desired output tracking performance. A second-order sliding mode strategy is developed to control the AFR to remove the effects of time-varying delay, canister purge disturbance, and measurement noise. The chattering-free response of the proposed controller is compared with conventional dynamic sliding mode control. The results of applying the proposed method to the experimental data demonstrate improved closed-loop system responses for various operating conditions.

Specific change in spectral power of fetal heart rate variability related to fetal acidemia during labor: Comparison between preterm and term fetuses

BACKGROUND Spectral analysis of fetal heart rate (FHR) variability is a useful method to assess fetal condition. There have been several studies involving the change in spectral power related to fetal acidemia, but the results have been inconsistent. AIMS To determine the change in spectral power related to fetal umbilical arterial pH at birth, dividing cases into preterm (31-36 weeks) and term (≥37 weeks) gestations. STUDY DESIGN Case-control study. The 514 cases of deliveries were divided into a low-pH group (an umbilical arterial pH <7.2) and a control group (pH≥7.2). SUBJECTS FHR recorded on cardiotocography during the last 2h of labor. OUTCOME MEASURES The spectral powers in various bands of FHR variability. RESULTS In preterm fetuses, the total, low (LF), and movement (MF) frequency spectral powers and LF/HF ratio were significantly lower in the low-pH group than the control group (all P<0.05). In contrast, in term fetuses, the total frequency, LF, and MF powers were significantly higher in the low-pH group than the control group (all P<0.05). The area under the receiver operating characteristic of LF power to detect a low pH at birth was 0.794 in preterm fetuses and 0.595 in term fetuses. The specificity was 86.8% and 93.3% in preterm and term fetuses, respectively. CONCLUSIONS The changes in spectral power responding to a low pH are different between term and preterm fetuses. Spectral analysis of FHR variability may be useful fetal monitoring for early detection of fetal acidemia.

A novel wavelet-based index to detect epileptic seizures using scalp EEG signals

In this paper, we propose a novel wavelet-based algorithm for the detection of epileptic seizures. The algorithm is based on the recognition of rhythmic activities associated with ictal states in surface EEG recordings. Using a moving-window analysis, we first decomposed each EEG segment into a wavelet packet tree. Then, we extracted the coefficients corresponding to the frequency band of interest defined for rhythmic activities. Finally, a normalized index sensitive to both the rhythmicity and energy of the EEG signal was derived, based on the resulting coefficients. In our study, we evaluated this combined index for real-time detection of epileptic seizures using a dataset of ∼11.5 hours of multichannel scalp EEG recordings from three patients and compared it to our previously proposed wavelet-based index. In this dataset, the novel combined index detected all epileptic seizures with a false detection rate of 0.52/hr.

Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG

A novel real-time patient-specific algorithm to predict epileptic seizures is proposed. The method is based on the analysis of the positive zero-crossing intervals in the scalp electroencephalogram (EEG), describing the brain dynamics. In a moving-window analysis, the histogram of these intervals in each EEG epoch is computed, and the distribution of the histogram value in specific bins, selected using interictal and preictal references, is estimated based on the values obtained from the current epoch and the epochs of the last 5 min. The resulting distribution for each selected bin is then compared to two reference distributions (interictal and preictal), and a seizure prediction index is developed. Comparing this index with a patient-specific threshold for all EEG channels, a seizure prediction alarm is finally generated. The algorithm was tested on ∼15.5 hours of multichannel scalp EEG recordings from three patients with temporal lobe epilepsy, including 14 seizures. 86% of seizures were predicted with an average prediction time of 20.8 min and a false prediction rate of 0.12/hr.

Automated analysis of ECG waveforms with atypical QRS complex morphologies

Abstract Automated detection of the various features of an electrocardiogram (ECG) waveform has wide applications in clinical diagnosis. Although detection of typical QRS waveforms has been widely studied, detection of atypical waveforms with complex morphologies remains challenging. The importance of detecting these complex waveforms and their patterns has grown recently due to their clinical implications. In this paper, we propose a novel algorithm for detecting the various peaks of such complex ECG waveforms. It is identified that most of the well-formed ECG waveforms – both typical and complex – fall into nine broad categories according to the standard nomenclature. Motivated by this ECG waveform classification, our algorithm uses signal analysis techniques such as first and second derivatives and adaptive thresholds to classify these waveforms accordingly by detecting the various features present in them. Temporal coherence along a single lead as well as spatial coherence across the 12 leads are used to improve performance. For waveform and pattern analysis, data from 50 healthy subjects and 50 patients with myocardial infarction were randomly selected. Results with an overall sensitivity of 99.06% and overall positive predictive value of 98.89% validate the effectiveness of the approach. Further, the algorithm gives true detections even on waveforms with fluctuations in baseline and wave amplitudes, proving its robustness against such variations.

Detection of epileptic seizures in scalp electroencephalogram: an automated real-time wavelet-based approach.

Summary This study evaluates a new automated patient-specific method for epileptic seizure detection using scalp electroencephalogram (EEG). The method relies on a normalized wavelet-based index, named the combined seizure index (CSI), and requires a seizure example and a nonseizure EEG interval as reference. The CSI is derived for every epoch in each EEG channel and is sensitive to both the rhythmicity and relative energy of that epoch and the consistency of EEG patterns among different channels. Increasing significantly as seizures occur, the CSI is monitored using a one-sided cumulative sum test to generate appropriate alarms in each channel. A seizure alarm is finally generated according to channel-based information. The proposed method was evaluated using the scalp EEG test data of approximately 236 hours from 26 patients with a total of 79 focal seizures, achieving a high sensitivity of approximately 91% with a false detection rate of 0.33 per hour and a median detection latency of 7 seconds. In addition, statistical analysis revealed that the average CSI around the onset on the side of the focus in patients with temporal lobe epilepsy (TLE) is significantly greater than that of the opposite side (P < 0.001), indicating the capability of this index in lateralizing the seizure focus in this type of epilepsy.

A performance comparison of hand motion EMG classification

Powered prosthesis is of considerable value to amputees to enable them to perform their daily-life activities with convenience. One of applicable control signals for controlling a powered prosthesis is the myoelectric signal. A number of commercial products have been developed that utilize myoelectric control for powered prostheses; however, the functionality of these devices is still insufficient to satisfy the needs of amputees. For the purpose of a comparison, several electromyogram classification methods have been studied in this paper. The performance criteria included not only classification accuracy, but also repeatability and robustness of the classifier, training time for online training performance, and computational time for real-time operation were evaluated with seven classification algorithms. The study included five different feature sets with time-domain feature values and autoregressive model coefficients. In summary, the quadratic discriminant analysis showed a remarkable performance in terms of high classification accuracy, high robustness, and low computational time of training and classification from the experiment results.