Ventricular fibrillation detection from ECG surface electrodes using different filtering techniques, window length and artificial neural networks
Medical personnel face many difficulties when diagnosing ventricular fibrillation (VF). Its correct diagnosis allows to decide the right medical treatment and, therefore, it is essential to tell it apart adequately from ventricular tachycardia (VT) and other arrhythmias. If the required therapy is not appropriate, the personnel could cause serious injuries or even induce VF. In this work, a diagnosis automatic system for the detection of VF through feature extraction was developed. To verify the validity of this method, an Artificial Neural Network (ANN) classifier was used. The ECG signals used were obtained from the MIT-BIH Malignant Ventricular Arrhythmia Database and AHA (2000 series) d…
Automatic screening of cardiac disorders using wavelet analysis of heart sound
Body auscultation is a dominant method for physical examination of human heart using conventional stethoscope. This clinical method is non invasive and efficient but it requires a medical expert to interpret the heart sound for assessment of cardiac disorders. This paper presents analysis of heart sounds in wavelet domain for automated screening of cardiac disorders. Heart sound signal is transformed in wavelet domain to find out discrimination between heart sounds recorded from healthy and anomalous patients. Discriminatory features extracted from wavelet coefficients of heart sound are subjected to machine learning for screening of cardiac disorders automatically. The proposed method for …
A novel pilot study of automatic identification of EMF radiation effect on brain using computer vision and machine learning
Abstract Electromagnetic field (EMF) radiations from mobile phones and cell tower affect brain of humans and other organisms in many ways. Exposure to EMF could lead to neurological changes causing morphological or chemical changes in the brain and other internal organs. Cellular level analysis to measure and identify the effect of mobile radiations is an expensive and long process as it requires preparing the cell suspension for the analysis. This paper presents a novel pilot study to identify changes in brain morphology under EMF exposure considering drosophila melanogaster as a specimen. The brain is automatically segmented, obtaining microscopic images from which discriminatory geometri…
Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction
Due the fact that the required therapy to treat Ventricular Fibrillation (V F) is aggressive (electric shock), the lack of a proper detection and recovering therapy could cause serious injuries to the patient or trigger a ventricular fibrillation, or even death. This work describes the development of an automatic diagnostic system for the detection of the occurrence of V F in real time by means of the time-frequency representation (T F R) image of the ECG. The main novelties are the use of the T F R image as input for a classification process, as well as the use of combined classifiers. The feature extraction stage is eliminated and, together with the use of specialized binary classifiers, …