Rajveer Shastri

Feature extraction of ECG signal

Electrocardiogram (ECG) is one the important biomedical signal. One heartbeat of ECG consists of different segments such as QRS complex, ST segment and PR segment. Features of an ECG signal are nothing but these segments and intervals between fiducial points such as RR interval, amplitude of P, R and T wave. Several techniques are discovered and are still developing for analyzing ECG signal. Some of them are Continuous Wavelet Transform, Discrete Wavelet Transform and Pan Tompkins Algorithm. In this paper, with the help of extracted dynamic feature 3 different types of arrhythmia have been detected using discrete wavelet transform and thresholding method. This system is validated on standard MIT-BIH arrhythmia database and it yields about 85% of sensitivity.

Epileptic electroencephalogram classification

Brain is very complex organ of the body. Electrical activity of the brain is referred as Electroencephalogram (EEG). EEG signal acts as a n important tool for diagnosis of neural diseases. The problem of EEG signal classification is a pattern recognition problem using extracted features. Observing EEG signals for seizure detection all times is a difficult task. So to make the detection easier, increase the accuracy and speed of analysis & classification it is necessary to invent different techniques. In this paper, to extract characteristics from given EEG data different methods of feature extraction have been applied. Features have been extracted by computing Fourier transform, discrete wavelet transform (DWT), empirical mode decomposition (EMD) & bispectrum analysis and seizure detection pattern is investigated for classification. The database which is publicly available at Bonn University is taken.

Analysis of adaptive filtering techniques for fresh water dolphin signals in their natural habitat

Tropical shallow waters typically present poor Signal to Noise Ratio (SNR) for any underwater system. Fresh water habitats experience heavy boat traffics due to significant human encroachments and sharing of habitat with other local species. The Irrawaddy dolphins are known to be facultative species with the significant human presence in these habitats. The boat traffic in their habitat is an important source of noise that degrades their acoustic habitat and even impacts the performance of sonars deployed for monitoring their activities and habitat of these species. The dynamic underwater channel fluctuations of the shallow tropical waters make the design of filters extremely complicated for any SNR enhancement initiative. The dynamic nature of the marine channel originates from the time, frequency and spatial fluctuations of the tropical shallow water environment with varying boundary conditions and multiple boundary interactions as the signal propagates from the source to the receiver. It is unable to track the changes of signal and noise using the fixed coefficient filter in such applications. This work attempts to compare the performance of two adaptive filters to enhance the SNR for dolphin signals in such ambient noise conditions. The two adaptive algorithms LMS (Least Mean Squares) and NLMS (Normalized Least Mean Squares) have been evaluated by comparing the performance parameters such as SNR (Signal-to-Noise Ratio) and MSE (Mean Square Error). The input signal in the work is Irrawaddy dolphin signal in Chilika Lake (19.8450° N, 85.4788° E), that is degraded due high boat traffic of dolphin watching tourist boats. The spectral and the temporal characteristics of recovered signal is verified using the spectrogram method. The simulation study is undertaken using available dolphin click signal and boat noise to be able to identify the precise SNR of the signal at receiver for accurate performance evaluation of the proposed noise mitigation algorithms. This comparative study shows that the NLMS is a better adaptive algorithm for filtering and thus, can improve the performance of a sonar system.

On the selection of time frequency feature set to represent passive sonar data of tropical waters

The characterization of underwater ambient noise using time frequency representations (TFRs) is challenging due to the opulence of the potential information that can be extracted from it. Traditionally Short Term Fourier Transform (STFT) is used to observe underwater ambient noise sources from the signal received by sonar. Short term Fourier Transform is a class of distributions determined by window size and shape. Wavelets also have unbound applications to represent the Sonar data. In this paper, statistical and image quality parameter sets are used to represent time frequency features of littoral water ambient noise. These feature sets are compared on variance scale by applying TFRs to different combinations of natural and anthropogenic ambient noise sources recorded at the west coast of India.

Analysis of ship noise from underwater ambient noise

Underwater signal processing includes several application areas like military application, disaster detection, finding natural resources, etc. When signal transmits through water, it may get overlap due to some inherent mechanisms like ship noise, wind noise, marine mammals noise, noise generated during all stages of oil production, and due to other industrial sources. In this paper, the main focus is an analysis of ship and wind noise present in data recorded on the west coast of India. The analysis is based on spectral characteristics of ship noise from the available signal. Identification of noise from the signal is carried out by using spectrogram and edge detection. Then shipping noise is mitigated with the help of discrete wavelet transform and signal to noise ratio is calculated to validate its performance. Software tools like MATLAB, Sigview are used for analysis.

Non-Gaussianity and non-stationarity detection in underwater ambient noise using hypothetical tests

In underwater signal processing, acoustic signals are used to transmit and receive information for communication. But the acoustic signals get affected due to spreading, reverberation, attenuation and absorption which produce ambient noise. Ambient noise is the background noise created due to natural sources as well as anthropogenic (man-made) sources. Ambient noise can be characterized as Gaussian, nonGaussian, stationary and non-stationary because of what the sonar system get disturbed. So to improve sonar system performance, characterization of ambient noise is important. In this paper, non-Gaussianity in tropical waters of the western coast of India is compared using various hypothetical tests like Lilliefors test, Anderson-Darling test, and Shapiro-Wilk test. Non-stationarity is identified using Kolmogorov-Smirnov two-sample test.

Wavelet denoising with ICA for the segmentation of bio-acoustic sources in a noisy underwater environment

In this paper, comparative analysis of ICA algorithms has been done for separation of biological sounds in underwater noisy environment. Dolphin whistles, echolocation clicks and snapping shrimps are mixed to form underwater noisy environment. Various ICA algorithms like Fast-ICA, Infomax are applied on mixture of bio-signals. The efficiency of these algorithms has been compared using performance of separability. Signals available in open source and ICALAB based ICALAB simulation packages have been used to compare ICA algorithms.