Usha sudheer Desai

South East Asian Education Trust · Department of Electronics and Communication Engineering

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This chapter provides essential information about electroencephalography (EEG) signal analysis for the brain-computer interface (BCI). The BCI translates the brain activity signals of the users into commands or messages for carrying out specific tasks. The main objective of the BCI is to aid people with neuromuscular diseases by analyzing and trans...

In the past, sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security purposes. However, relying on eyewitness observations can lead to discrepancies in the depictions of the sketch, depending on the experience and skills of the sketch artist. With the emergence of m...

Brain-Computer Interface is an emerging field that focuses on transforming brain data into machine commands. EEG-based BCI is widely used due to the non-invasive nature of Electroencephalogram. Classification of EEG signals is one of the primary components in BCI applications. Steady-State Visually Evoked Potential (SSVEP) paradigms have gained imp...

Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin (Type I or insulin-dependent diabetes) or when the body cannot effectively use the insulin it produces (Type II or non-insulin-dependent diabetes). Insulin is a hormone that regulates Blood Glucose Levels (BGLs). The global number of people with diabe...

Hilbert-Huang Transform (HHT) is NASA's designated name for the combination of the Empirical Mode Decomposition (EMD) and the Hilbert Spectral Analysis (HSA). The Empirical Mode Decomposition (EMD) decomposes any complicated signal into a group of Intrinsic Mode Functions (IMFs) which are AM and FM modulated waveforms. The final presentation of the...

Currently, the operational electroencephalography (EEG)-based brain–computer interfaces (BCIs) suffer from problems of BCI latency/lag issues, which restricts the use of interfaces impractical scenarios. One of the reasons behind the present challenges is the application of a purely data-driven approach to the BCI pipeline. Although BCI application...

Abstract: Doctors face difficulty in diagnosis of lung cancer due to the complex nature and clinical interrelations of computer diagnosed scan images.. Hence the visual inspection and subjective evaluation methods are time consuming and tedious, which leads to inter and intra observer inconsistency or imprecise classification. The Computer-Aided De...

Deep Learning Technique (DLT) is the sub-branch of Machine Learning (ML) which assists to learn the data in multiple levels of representation and abstraction and shows impressive performance on many Artificial Intelligence (AI) tasks. This paper presents a new method to analyse the healthcare data using DLT algorithms and associated mathematical fo...

Coronary Artery Disease (CAD) is the most leading Cardiovascular Disease (CVD), which results due to buildup of plaque inside the coronary arteries. The CAD and Normal Sinus Rhythm (NSR) heartbeats can be discriminated and diagnosed noninvasively using the standard tool Electrocardiogram (ECG). However, manual diagnosis of ECG is tiresome and time...

Myocardial ischemia (MI) is abnormal cardiac condition during which myocardium is deprived of blood and oxygen supply. It is the most prominent heart disease around the globe, and its prevalence increases with age factors. Accurate and timely identification and treatment of MI help to prevent from series of dreadful ischemic heart diseases such as...

Electrocardiogram (ECG) is the most reliable and low-cost diagnostic tool to evaluate the patients with cardiac arrhythmias. Manual diagnosis of arrhythmia beats is very tedious due to the nonlinear and complex nature of ECG. The current paper, describes pattern recognition and machine learning-based approach for computer-aided detection of five cl...

An intelligent tachycardia diagnosis system assists the clinicians in discriminating normal and various tachycardia classes of heartbeats generally in the life-threatening conditions. This paper proposes, a methodology to classify multiclass tachycardia class using Electrocardiogram (ECG) signal. In this work, tachycardia classes are marked using n...

Atrial Fibrillation (A-Fib), Atrial Flutter (AFL) and Ventricular Fibrillation (V-Fib) are fatal cardiac abnormalities commonly affecting people in advanced age and have indication of life-threatening condition. To detect these abnormal rhythms, Electrocardiogram (ECG) signal is most commonly visualized as a significant clinical tool. Concealed non...

Electrocardiogram (ECG) signal is a non-invasive method, used to diagnose the patients with cardiac abnormalities. The subjective evaluation of interval and amplitude of ECG by physician can be tedious, time consuming, and susceptible to observer bias. ECG signals are generated due to the excitation of many cardiac myocytes and hence resultant sign...

Electrocardiogram (ECG) remains the most reliable and low-cost diagnostic tool to evaluate the patients with cardiac arrhythmias. Manual diagnosis of arrhythmia beats is very tedious due to the nonlinear and complex nature of ECG. Likewise, minute variations in time-domain features viz. amplitude, segments and intervals are difficult to interpret b...

Arrhythmia is an abnormal beat result from any disorder in the conduction of the cardiac electrical impulse. It is very essential to identify and detect arrhythmias correctly in its early phases. Manual diagnosis of arrhythmia beats is very difficult due to their unfamiliar mechanisms and composite nature. Current paper introduces, a machine learni...

Elimination of baseline drift is required in order to minimize the changes in electrocardiogram beat morphology. Particularly, low-frequency ST-T segment measures are strongly affected by the baseline artifact. This paper presents an efficient algorithm for baseline drift correction using wavelet transform multiresolution analysis, this can be achi...

Image fusion refers to the process of combining the relevant information from two or more images into a single highly informative image. In this paper, the images obtained from different medical imaging techniques such as Computer Tomography (CT), Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) will be fused. Initially brain...