A. Fred, K. N, V. Suresh, R. Mathew, Reethu Reji, S. S. Mathews
{"title":"Hardware Implementation of Heart Rate and QRS Complex Detection Using Raspberry Pi Processor for Medical Diagnosis","authors":"A. Fred, K. N, V. Suresh, R. Mathew, Reethu Reji, S. S. Mathews","doi":"10.1109/ICRAECC43874.2019.8995169","DOIUrl":"https://doi.org/10.1109/ICRAECC43874.2019.8995169","url":null,"abstract":"Electrocardiogram signals are acquired from the human body for the diagnosis of cardiac disorders. The surface electrodes are used for ECG signal acquisition and prior to hear beat detection preprocessing is performed. The FIR band pass filter based on Kaiser window is used for the filtering of the ECG signal. The band pass filtered energy signal is subjected to thresholding algorithm for R peak detection. The heart rate is estimated from the R-R interval. The hybrid filter with thresholding was employed for the QRS complex detection. The algorithms are developed in python and implemented in raspberry Pi embedded processor. The algorithms are evaluated on fantasia ECG data set and satisfactory results are obtained.","PeriodicalId":137313,"journal":{"name":"2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127180005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Judson, DARWIN. P, T. A. MARIA DEVI, Chachu N Helen
{"title":"Performance Analysis of Image Transmission with Different Transforms in MC-CDMA","authors":"D. Judson, DARWIN. P, T. A. MARIA DEVI, Chachu N Helen","doi":"10.1109/ICRAECC43874.2019.8995128","DOIUrl":"https://doi.org/10.1109/ICRAECC43874.2019.8995128","url":null,"abstract":"In the field of communication, multimedia transmission is very important. Transmission of high quality images through wireless channel have always been challenging. This is due to the reserved bandwidth and capacity. So instead of conventional method of transmitting image with one carrier, the image signal is divided into subsets and different sub band carrier modulates each subset. So the main objective is to transmit an image in a multicarrier modulation system with good quality in a hostile radio channel. Multi carrier Code Division Multiple Access (MC-CDMA) is a multiple access scheme which combines both the benefits of orthogonal frequency division multiplexing (OFDM) and code division multiple access (CDMA). In the proposed MC-CDMA system, Discrete Cosine Transform (DCT) is used instead of Discrete Fourier Transform (DFT) because of its spectral energy compaction property. Further to overcome the peak to average power ratio (PAPR) problem, trigonometric transforms based OFDM are used in MC-CDMA system. The effects of multi access interference (MAI) can be counteracted by frequency domain equalization (FDE). The performance of the proposed system is evaluated using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) criterion.","PeriodicalId":137313,"journal":{"name":"2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115093524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Medical Image Registration Using Coral Reef Optimization for Substrate Layer","authors":"Babin T Praise, Jovina Gilbert J","doi":"10.1109/ICRAECC43874.2019.8995120","DOIUrl":"https://doi.org/10.1109/ICRAECC43874.2019.8995120","url":null,"abstract":"Image registration is process of alignment of two or more images. It is used to get more information about the obtained image which is useful for the analysis of the disease. In the proposed method we use three different types of image registration. They are demon registration, image registration by mutual information and difference method. In these methods the alignment is achieved by changing some parameters manually. The degree of alignment of two images is directly proportional to the amount of information obtained. In order to maximize the alignment an optimization algorithm is used. The conventional image registration methods are constrained by many limitations. Hence we use a bio-inspired meta-heuristics and high performance Coral Reef optimization with Substrate Layer (CRO-SL) algorithm. CRO-SL is an advanced method of coral reef optimization based on natural behavior of coral reef. The image registration process comprises of various steps like transformation of registering image, evaluation of performance metrics and finding the optimized value for transformation. A uni-model affine transformation is used in the proposed method. The experimental results show that CRO-SL is a very efficient approach in case of alignment of image in higher degree.","PeriodicalId":137313,"journal":{"name":"2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116813592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Classification of ECG using convolutional neural network (CNN)","authors":"Dhakshaya Ss, D. J. Auxillia","doi":"10.1109/ICRAECC43874.2019.8995096","DOIUrl":"https://doi.org/10.1109/ICRAECC43874.2019.8995096","url":null,"abstract":"Electrocardiogram (ECG) gives the clear record on electrical activities of heart. This record can be used to diagnose various heart diseases. An approach is proposed to automatically detect the myocardial infraction using ECG signals. In this work, a convolutional neural network (CNN) algorithm is implemented for the automated detection of a normal and Abnormal ECG signals (Left bundle branch block beat (LBBB), Right bundle branch block beat (RBBB), Atrial premature beat (APB) and Paced beat (PB)). The feature extraction and signal classification both are carried in a single CNN unit. MIT-BIH arrhythmia database is used to obtain the five different classes of ECG signals. This proposed classifier accurately classifies the signals with reduced classification time. So, in clinical settings this method can be implemented to help the clinicians in the diagnosis of myocardial infarction.","PeriodicalId":137313,"journal":{"name":"2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)","volume":"55 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133239540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}