Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach.

OBJECTIVE: To deconstruct the epileptogenic networks of patients with drug-resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data. METHODS: We analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non-epileptiform EEG epochs (with or without spikes). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non-epileptogenic zones (NEZs) in the same and opposite hemispheres. Region-specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign-rank). We developed ML classifiers to identify the VSs in the EZ using VS-specific FC features. Cross-validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization. RESULTS: FC differed between EZ and NEZs (p < .05) during non-epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non-epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes. SIGNIFICANCE: We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.

Effect of Extra-abdominal Vein Varix on the Stress Distribution in Umbilical Cord: A Simulation Study.

The umbilical cord is a critical structure linking the fetus to the placenta and is surrounded by the amniotic fluid. It is composed of a vein, two arteries coiled around the vein, and Wharton's jelly surrounding the blood vessels. In this study, the stress distribution of the arteries, vein, and Wharton's jelly of an umbilical cord with extra-abdominal umbilical vein varix is analyzed for varying amniotic pressure using finite element analysis. Four diameters are considered for the umbilical vein, 6.5 mm, 11 mm, 15.5 mm, and 20 mm, with 6.5 mm corresponding to the normal vein diameter. The amniotic pressure is varied from 15-105 mmHg in steps of 15 mmHg, to simulate contractions during labour. Stress distribution is obtained and the peak stresses are analyzed. According to the results, the peak stress in the Wharton's jelly and the umbilical vein increases nonlinearly with increasing amniotic pressure. The peak stress in umbilical arteries initially decreases till the amniotic pressure reaches 45 mmHg and thereafter increases. This might be due to asymmetric deformation of the Wharton's jelly at the pressure range below arterial pressure.Clinical Relevance- This study could be useful in understanding the fundamental mechanics of extra-abdominal umbilical vein varix and help in development of better treatment protocols.

Differentiation of urothelial carcinoma in histopathology images using deep learning and visualization.

Artificial Intelligence is a tool poised to transform healthcare, with use in diagnostics and therapeutics. The widespread use of digital pathology has been due to the advent of whole slide imaging. Cheaper storage for digital images, along with unprecedented progress in artificial intelligence, have paved the synergy of these two fields. This has pushed the limits of traditional diagnosis using light microscopy, from a more subjective to a more objective method of looking at cases, incorporating grading too. The grading of histopathological images of urothelial carcinoma of the urinary bladder is important with direct implications for surgical management and prognosis. In this study, the aim is to classify urothelial carcinoma into low and high grade based on the WHO 2016 classification. The hematoxylin and eosin-stained transurethral resection of bladder tumor (TURBT) samples of both low and high grade non-invasive papillary urothelial carcinoma were digitally scanned. Patches were extracted from these whole slide images to feed into a deep learning (Convolution Neural Network: CNN) model. Patches were segregated if they had tumor tissue and only included for model training if a threshold of 90% of tumor tissue per patch was seen. Various parameters of the deep learning model, known as hyperparameters, were optimized to get the best accuracy for grading or classification into low- and high-grade urothelial carcinoma. The model was robust with an overall accuracy of 90% after hyperparameter tuning. Visualization in the form of a class activation map using Grad-CAM was done. This indicates that such a model can be used as a companion diagnostic tool for grading of urothelial carcinoma. The probable causes of this accuracy are summarized along with the limitations of this study and future work possible.

Numerical Analysis of Temperature Distribution Profiles of Breast Tissues with Cyst and Tumor of Different sizes and Locations.

Breast cancer causes more deaths among all types of cancers. Efforts have been put to study the change in temperature distribution profile of the breast in presence of an abnormality. By applying Pennes's bio-heat equation, a 2D finite element model is developed for the heat transfer mechanism. Surface temperature gradients due to the presence of abnormalities at various depths and sizes are analyzed. The results show that the presence of a cyst decreases the temperature whereas the occurrence of tumor increases the temperature inside the breast. It is observed that abnormal tissue having a radius less than 1.5cm and depth greater than 5cm, has a negligible effect on the surface temperature profile. The highest change in surface temperature is observed when a cyst or tumor is larger and present near the skin. The simulation results help in the better interpretation of the thermal images and calibration of infrared camera. This study could be helpful in the early diagnosis of breast cancer.

Emotion Recognition Using Spectral Feature from Facial Electromygraphy Signals for Human-Machine Interface.

Recognition of the emotions demonstrated by human beings plays a crucial role in healthcare and human-machine interface. This paper reports an attempt to classify emotions using a spectral feature from facial electromyography (facial EMG) signals in the valence affective dimension. For this purpose, the facial EMG signals are obtained from the DEAP dataset. The signals are subjected to Short-Time Fourier Transform, and the peak frequency values are extracted from the signal in intervals of one second. Support vector machine (SVM) classifier is used for the classification of the features extracted. The extracted feature can classify the signals in the valence dimension with an accuracy of 61.37%. The proposed feature could be used as an added feature for emotion recognition, and this method of analysis could be extended to myoelectric control applications.

Spatial Intensity Map of HDEMG Based Classification of Muscle Fatigue.

In this, study, we have investigated to identify the muscle fatigue using spatial maps of High-Density Electromyography (HDEMG). The experiment involves subjects performing plantar flexion at 40% maximum voluntary contraction until fatigue. During the experiment, HDEMG signal was recorded from the tibialis anterior muscle. The monopolar and bipolar spatial intensity maps were extracted from the HDEMG signal. The random forest classifier with different tree configurations was tested to distinguish nonfatigue and fatigue condition. The results indicate that selected electrodes from the differential intensity map results in an accuracy of 83.3% with the number of trees set at 17. This method of spatial analysis of HDEMG signals may be extended to assess fatigue in real life scenarios.

Characterization of surface electromyography signals of biceps brachii muscle in fatigue using symbolic motif features.

Exercise-induced muscle damage is a condition which results in the loss of muscle function due to overexertion. Muscle fatigue is a precursor of this phenomenon. The characterization of muscle fatigue plays a crucial role in preventing muscle damage. In this work, an attempt is made to develop signal processing methods to understand the dynamics of the muscle's electrical properties. Surface electromyography signals are recorded from 50 healthy adult volunteers under dynamic curl exercise. The signals are preprocessed, and the first difference signal is computed. Furthermore, ascending and descending slopes are used to generate a binary sequence. The binary sequence of various motif lengths is analyzed using features such as the average symbolic occurrence, modified Shannon entropy, chi-square value, time irreversibility, maximum probability of pattern and forbidden pattern ratio. The progression of muscle fatigue is assessed using trend analysis techniques. The motif length is optimized to maximize the rho value of features. In addition, the first and the last zones of the signal are compared with standard statistical tests. The results indicate that the recorded signals differ in both frequency and amplitude in both inter- and intra-subjects along the period of the experiment. The binary sequence generated has information related to the complexity of the signal. The presence of more repetitive patterns across the motif lengths in the case of fatigue indicates that the signal has lower complexity. In most cases, larger motif length resulted in better rho values. In a comparison of the first and the last zones, most of the extracted features are statistically significant with p < 0.05. It is observed that at the motif length of 13 all the extracted features are significant. This analysis method can be extended to diagnose other neuromuscular conditions.

Classification of Biceps Brachii Muscle Fatigue Condition Using Phase Space Network Features.

In this, study, an attempt is made to differentiate muscle nonfatigue and fatigue condition using signal complexity metrics derived from phase space network features. A total of 55 healthy adult volunteers performed dynamic contraction of the biceps brachii muscle. The first and last curl are segmented and are considered as nonfatigue and fatigue condition respectively. A weighted phase space network is constructed and reduced to a binary network based on various radii. The mean and median degree centrality features are extracted from these networks and are used for classification. The results of the classification indicate that these features are capable of differentiating nonfatigue and fatigue condition with 91% accuracy. This method of analysis can be extended to applications such as diagnosis of neuromuscular disorder where fatigue is a symptom.

Automated classification of five seizure onset patterns from intracranial electroencephalogram signals.

OBJECTIVE: The electroencephalographic (EEG) signals contain information about seizures and their onset location. There are several seizure onset patterns reported in the literature, and these patterns have clinical significance. In this work, we propose a system to automatically classify five seizure onset patterns from intracerebral EEG signals. METHODS: The EEG was segmented by clinicians indicating the start and end time of each seizure onset pattern, the channels involved at onset and the seizure onset pattern. Twelve features that represent the time domain characteristics and signal complexity were extracted from 663 seizures channels of 24 patients. The features were used for classification of the patterns with support vector machine - Error-Correcting Output Codes (SVM-ECOC). Three patient groups with a similar number of seizure segments were created, and one group was used for testing and the rest for training. This test was repeated by rotating the testing and training data. RESULTS: The feature space formed by both time domain and multiscale sample entropy features perform well in classification of the data. An overall accuracy of 80.7% was obtained with these features and a linear kernel of SVM-ECOC. CONCLUSIONS: The seizure onset patterns consist of varied time and complexity characteristics. It is possible to automatically classify various seizure onset patterns very similarly to visual classification. SIGNIFICANCE: The proposed system could aid the medical team in assessing intracerebral EEG by providing an objective classification of seizure onset patterns.

Analysis of Dynamic Contractions from Biceps Brachii Muscle using Surface Electromyography signals and Multiscale Visibility Graph Features.

In this work, an attempt is made to characterize the variation in the complexity of the surface electromyography (sEMG) signals in fatigue. For this, sEMG signals from 58 healthy volunteers are recorded from the biceps brachii muscle under well-defined dynamic contraction protocol. The contractions are segmented, and the initial and final curls are extracted. These are considered as nonfatigue and fatigue respectively. Further, visibility graphs are constructed at multiple scales, and median degree centrality (MSMC) is calculated in them. To quantify the variations in the MSMC, two features namely, the average and standard deviation are calculated. The results reveal that the recorded signals are non-stationary. The constructed networks form distinct clusters in space. The MSMC feature shows a decreasing trend with scale in both nonfatigue and fatigue conditions. Additionally, the extracted features have higher values in fatigue. This may be due to the motor unit synchronization, which causes an increase in connectivity between nodes. All the extracted features showed statistical significance with p<; 0.005. This approach of analysis can be extended to characterize muscle in other neuromuscular conditions.

Analysis of Sequential Visibility Motifs in Isometric Surface Electromyography Signals in Fatiguing Condition.

Muscle fatigue is the inability to exert the required force. Surface Electromyography (sEMG) is a technique used to study the muscle's electrical property. These generated signals are complex and nonstationary in nature. In this work, an attempt is made to utilize graph signal processing methods such as Sequential Visibility motif for the analysis of muscle fatigue condition. The sEMG signals of 41 healthy adult volunteers are acquired from the biceps brachii muscle during isometric contraction with a 6 Kg load. The subjects are asked to perform the exercise until they are unable to continue. The signals are preprocessed, and the first and last 500 ms of the signal are considered for analysis. The segmented signals are subjected to sequential visibility graph algorithm. Further, the number of motifs for a subgraph of four is calculated. The results show that the signals are unique for each subject. The frequency of higher degree motif is more in the case of fatigue. The frequency of each unique motif is capable of differentiating nonfatigue and fatigue conditions. Nonparametric statistical test result indicates all features are significant with p<0.05. This method of analysis can be extended to other varied neuromuscular conditions.

Analysis of progression of fatigue conditions in biceps brachii muscles using surface electromyography signals and complexity based features.

Muscle fatigue is a neuromuscular condition where muscle performance decreases due to sustained or intense contraction. It is experienced by both normal and abnormal subjects. In this work, an attempt has been made to analyze the progression of muscle fatigue in biceps brachii muscles using surface electromyography (sEMG) signals. The sEMG signals are recorded from fifty healthy volunteers during dynamic contractions under well defined protocol. The acquired signals are preprocessed and segmented in to six equal parts for further analysis. The features, such as activity, mobility, complexity, sample entropy and spectral entropy are extracted from all six zones. The results are found showing that the extracted features except complexity feature have significant variations in differentiating non-fatigue and fatigue zone respectively. Thus, it appears that, these features are useful in automated analysis of various neuromuscular activities in normal and pathological conditions.