Goutam Kumar Sahoo
Other affiliations: Padmanava College of Engineering, Rourkela
Bio: Goutam Kumar Sahoo is an academic researcher from National Institute of Technology, Rourkela. The author has contributed to research in topic(s): The Internet & QRS complex. The author has an hindex of 2, co-authored 6 publication(s) receiving 13 citation(s). Previous affiliations of Goutam Kumar Sahoo include Padmanava College of Engineering, Rourkela.
••11 Apr 2013
TL;DR: The extracted ST-segment and T-wave features are used for detection of ischemic episodes and the performance of the method shows 88.08% sensitivity and 92.42% positive predictive accuracy.
Abstract: Electrocardiogram (ECG) is generally used for diagnosis of cardiovascular abnormalities and heart disorders. An efficient method for analyzing the ECG signal towards the detection of cardiovascular abnormalities and ischemic episodes follows mainly five stages: pre-processing, feature extraction,cardiac abnormality detection, beat classification and ischemic episode recognition.The detection of cardiovascular abnormalities like bradycardia and tachycardia is based on the calculation of heart rate(HR) from the extracted ECG features.The extracted ST-segment and T-wave features are used for detection of ischemic episodes.The ability of the method was tested on European ST-T database. The performance of ischemic episode detection shows 88.08% sensitivity (Se) and 92.42% positive predictive accuracy (PPA).
••05 Mar 2015
TL;DR: The experimental result shows that the proposed method achieves better compression ratio along with better PRD compared to earlier methods.
Abstract: Compression of bulky electrocardiogram (ECG) signal is a common requirement for most of the computerized applications. In this paper, a new compression and reconstruction technique based on Empirical Mode Decomposition (EMD) is proposed. The performance evaluation of the proposed technique is based on comparisons of Compression Ratio (CR) and Percent Root mean square Difference (PRD). The compression method consists of mainly five stages: EMD based signal decomposition, downsampling, discrete cosine transform (DCT), window filtering and Huffman encoding. The ECG signal reconstruction method follows the compression process in reverse order. The proposed algorithm is validated by testing on 48 ECG records available in MIT/BIH arrhythmia database. The compression efficiency is evaluated and the average values of CR and PRD are found to be 23.74:1 and 1.49, respectively. The experimental result shows that the proposed method achieves better compression ratio along with better PRD compared to earlier methods.
11 Sep 2020
TL;DR: The work presented here is a fast information sharing system with the date and time of the event, the detailed geographical location with Google map URL, speed, and the path traced using global positioning system (GPS) data.
Abstract: Road traffic crashes are the major risk factor in everyone’s day-to-day life due to the fast increase in the number of vehicles. It is now a more challenging task to deal with existing traffic systems with massive traffic. In the current scenario, the Internet of things (IoT) based solutions will make the transport system more intelligent. This solution will help to reduce congestion and increase the safety of everyday people. Fast reporting of driving abnormalities1 will help in preventing the life of the person involved in traffic crashes. Traffic crashes can be addressed using sensors, audio, and video-based analysis. The work presented here is a fast information sharing system with the date and time of the event, the detailed geographical location with Google map URL, speed, and the path traced using global positioning system (GPS) data. The fast reporting system uses various sensors to collect the vehicle data and notifies the driving abnormality, whenever any abnormal situation occurs. It will help the rescue team, insurance people, or relatives for easy navigation to the desired location. The limitations of communication bandwidth requirement and the powerful central processor may reduce detection performance; however, fast reporting could save many precious lives from danger.1 In this article, the abnormality is the behavior of the vehicle when it meets an accident while on the move.
24 Nov 2021-Evolutionary Intelligence
TL;DR: In this paper, a comparative evaluation of different classical as well as ensemble machine learning models, which are used to predict the risk of diabetes from two different datasets, i.e., PIMA Indian diabetes dataset and early-stage diabetes risk prediction dataset, is provided.
Abstract: Recently machine learning algorithms are widely used for the prediction of different attributes, and these algorithms find widespread applications in a variety of domains. Machine learning in health care has been one of the core areas of research where machine learning models are used on the medical datasets to predict different attributes. This work provides a comparative evaluation of different classical as well as ensemble machine learning models, which are used to predict the risk of diabetes from two different datasets, i.e., PIMA Indian diabetes dataset and early-stage diabetes risk prediction dataset. From the comparative analysis, it is found that the superlearner model provides the best accuracy i.e. 86% for PIMA Indian diabetes dataset, and it provides 97% accuracy for diabetes risk prediction dataset.
••01 Jan 2021
TL;DR: In this article, the authors proposed an early solution to day-to-day traffic incidents through real-time support to the people through the Internet of Things (IoT) through an Accident Prevention and Detection System (APDS).
Abstract: Road accidents are the major risk factor in day-to-day life. The prevention and quick detection of an accident is the priority in saving the lives of human beings. Advances in technologies like the internet of things (IoT) make life better for everyone but adding technologies to control and manage traffic in a smarter way is a big challenge. Accident prevention and detection system (APDS) is developed to provide real time support to the people through IoT. The APDS aims to provide an early solution to day-to-day traffic incidents. The prevention of accidents is more important as vehicles are controlled by human beings. The parameters, like change in speed, human body part movements, overtaking, rule braking, etc., are responsible for the accident. It can be managed or controlled using some rule-based techniques. The abnormal behavior of each parameter can be identified by continuous monitoring, and reporting the same well in before may reduce the occurrence of an accident. Once an accident occurs, the detail information of accident data are shared with end-users with some proper authentication. The information sharing is established through machine-to-machine (M2M) communication. The end-users will get all the data regarding location, time of the accident, and many more details by accessing the web link through the internet.
••01 Apr 1987
••23 Jul 2020
TL;DR: The distinctive features, merits, and demerits of the latest mobile phone processors of different Tech companies are discussed.
Abstract: Cell phones have become a necessity for many people throughout the world. The ability to keep in touch with family, business associates, and access to email are only a few of the reasons for the increasing importance of cell phones. However, the mobile-phones in early times were bulky, restrictive to only some features and worked only in areas where there was a good connection. All these problems were resolved by integrating a processor within a cell-phone. The processor is the central hub of your smartphone. It receives and executes every command, performing billions of calculations per second. The effectiveness of the processor directly affects every application you run, whether it's the camera, the music player, or just a simple email program. In the following journal, we have discussed the distinctive features, merits, and demerits of the latest mobile phone processors of different Tech companies.
••09 Feb 2017
TL;DR: An efficient electrocardiogram (ECG) data compression algorithm for tele-monitoring of cardiac patients from rural area, based on combination of two encoding techniques with discrete cosine transform, which provides good compression ratio (CR) with low percent root-mean-square difference (PRD) values.
Abstract: This paper reports an efficient electrocardiogram (ECG) data compression algorithm for tele-monitoring of cardiac patients from rural area, based on combination of two encoding techniques with discrete cosine transform. The proposed technique provides good compression ratio (CR) with low percent root-mean-square difference (PRD) values. For performance evaluation of the proposed algorithm 48 records of ECG signals are taken from MIT-BIH arrhythmia database. Each record of ECG signal is of duration 1 minute and sampled at sampling frequency of 360 Hz. Noise of the ECG signal has been removed using Savitzky-Golay filter. To transform the signal from time domain to frequency domain, discrete cosine transform has been used which compacts energy of the signal to lower order of frequency coefficients. After normalisation and rounding of transform coefficients, signals are encoded using dual encoding technique which consists of run length encoding and Huffman encoding. The dual encoding technique compresses data significantly without any loss of information. The proposed algorithm offers average values of CR, PRD, quality score, percent root mean square difference normalised, RMS error and SNR of 11.49, 3.43, 3.82, 5.51, 0.012 and 60.11 dB respectively.
01 Sep 2016
TL;DR: In this proposed method diseases are modeled using the time domain features of ECG signal which are extracted using BIOPAC AcqKnowledge software, which can be used to detect cardiac arrhythmia.
Abstract: Electrocardiogram (ECG) gives useful information about morphological and functional details of heart which is used to predict various cardiac diseases. In this paper a method of detecting cardiac diseases using support vector machine (SVM) is proposed. In this proposed method diseases are modeled using the time domain features of ECG signal which are extracted using BIOPAC AcqKnowledge software. Raw ECG signal contains these useful features which can be used to detect cardiac arrhythmia. The various ECG parameters like heart rate, QRS complex, PR interval, ST segment elevation, ST interval of ECG signal are used for analysis. Based on these parameters of ECG signal, different heart disease like atrial fibrillation, sinus tachycardia, myocardial infarction and apnea are detected. The individual accuracy of tachycardia arrhythmia, MI arrhythmia, atrial fibrillation arrhythmia and apnea proposed by SVM are 83.3%, 86.4%, 88% and 85.7% respectively.
••01 Nov 2014
TL;DR: This method, succeeding in differentiating the Abnormal ECG signals from the Normal signals, is proved to be a novel method for Auto analysis ofECG signals.
Abstract: The greatest challenge faced during the process of diagnosis of cardiovascular diseases is the accurate analyses of the Electrocardiogram (ECG). Many researches are being done to classify and analyze the ECG signals automatically. In this paper, a novel method for the Auto analysis of the ECG signals using MATLAB is proposed and implemented. In this method, the raw ECG data obtained from the patient goes through a process of Wavelet Packet Decomposition (WPD) followed by Feature extraction. The classification is further done using Artificial Neural Network (ANN). This method, succeeding in differentiating the Abnormal ECG signals from the Normal signals, is proved to be a novel method for Auto analysis of ECG signals.