Srinivasan Jayaraman

Human Electrocardiogram for Biometrics Using DTW and FLDA

This paper proposes a new approach for person identification and novel person authentication using single lead human Electrocardiogram. Nine Feature parameters were extracted from ECG in spatial domain for classification. For person identification, Dynamic Time Warping (DTW) and Fisher’s Linear Discriminant Analysis (FLDA) with K-Nearest Neighbor Classifier (NNC) as single stage classification yielded a recognition accuracy of 96% and 97% respectively. To further improve the performance of the system, two stage classification techniques have been adapted. In two stage classifications FLDA is used with k-NNC at the first stage followed by DTW classifier at the second stage which yielded 100% recognition accuracy. During person authentication we adapted the QRS complex based threshold technique. The overall performance of the system was 96% for both legal and intruder situations is verified for MIT-BIH normal database size of 375 recording from 15 individual ECG.

A Novel Technique for ECG Morphology Interpretation and Arrhythmia Detection Based on Time Series Signal Extracted from Scanned ECG Record

Cardiovascular disease (CVD) is the one of the biggest health problem in Indian and around the world as well. Electrocardiogram is a traditional method used for the diagnosis of heart diseases for about a century. Maintaining and retrieving patient history during a course of treatment is a essential but a laborious process. More particularly, over a decade ago thermal ECG records were stored physically, off late, due to advancement in technology; it has been stored as scanned ECG images. Storing the scanned ECG trace images requires considerable storage space. This necessitated the development of an automated solution capable of storing the ECG digitally, retrieving it quickly and detecting cardiac arrhythmia automatically. Majority of the ECG’s clinical information is said to be found in the intervals and amplitudes defined by its features (characteristic wave peaks and time durations).According to author’s knowledge, very few researchers [Lawson et al., 1995, Silva et al., Wang et al., 2009, Chebil et.al., 2008, Kao et al.,2001] have approached the extraction of ECG digital time series signal from scanned ECG trace images. Lawson et al., chose a scanning resolution of 200 dpi and used global thresholds to separate the ECG trace from the background grid lines. The low resolution results in loss of data accuracy and global thresholds results in missing pixels which are replenished by linear interpolation. Fabio Badilini et al., 2005 developed an application for extraction of the ECG trace from the image. But the method requires the user to fix anchor points for missing peaks and thus the accuracy comes down. Shen et al., separated the ECG trace from the background grids using the histogram. The missing pixels are replenished by checking the value of the pixel in the original image. This is a tedious process. Kao et al., employed a color filter to remove the background gridlines in the color image. There was a problem of missing pixels in the process which was replenished by linear interpolation. Jalel Chebil et al., performed a comparative study of the extracted trace accuracy by scanning the image at various resolutions. Global thresholds and median filtering were employed to remove background grids. The threshold to separate the trace from the background should be selected based on the nature of the image to avoid any

PerDMCS: Weighted Fusion of PPG Signal Features for Robust and Efficient Diabetes Mellitus Classification.

Non-invasive detection of Diabetes Mellitus (DM) has attracted a lot of interest in the recent years in pervasive health care. In this paper, we explore features related to heart rate variability (HRV) and signal pattern of the waveform from photoplethysmogram (PPG) signal for classifying DM (Type 2). HRV features includes timedomain (F1), frequency domain ( F2), non-linear features ( F3) where as waveform features ( F4) are one set of features such as height, width, slope and durations of pulse. The study was carried out on 50 healthy subjects and 50 DM patients. Support Vector Machines (SVM) are used to capture the discriminative information between the above mentioned healthy and DM categories, from the proposed features. The SVM models are developed separately using different sets of features F1, F2, F3,andF4, respectively. The classification performance of the developed SVM models using time-domain, frequency domain, non-linear and waveform features is observed to be 73%, 78%, 80% and 77%. The performance of the system using combination of all features is 82%. In this work, the performance of the DM classification system by combining the above mentioned feature sets with different percentage of discriminate features from each set is also examined. Furthermore weight based fusion is performed using confidence values obtained from each model to find the optimal set of features from each set with optimal weights for each set. The best performance accuracy of 89% is obtained by scores fusion where combinations of mixture of 90% features from the feature sets F1 and F2 and mixture of 100% features from the feature sets F3 andF4, with fusion optimal weights of 0.3 and 0.7, respectively.

A novel method to extract ECG morphology from scanned ECG records

ECG is a valuable source of information regarding the patients clinical data repository. Archiving the paper Electrocardiogram (ECG) trace as an image requires immense storage space and manpower for storage and retrieval of the patient records. Objective of this paper is to extract the ECG Morphological features from the paper ECG. In this paper, we have proposed a novel technique to extract the ECG Morphological features. The proposed algorithm has been evaluated with 25 patients ECG sheets printed from 12-lead ECG equipments. Further, the proposed technique enhances the accuracy of heart rate and morphological feature extraction from the obtained time series signal. The evaluation of the digital time series data conversion algorithm has been performed by comparing obtained heart rate with value printed on the paper. The accuracy of the heart rate was found to be 99.12%. Evaluation of the proposed slope method for ECG morphological feature extraction was done by comparing the obtained values with manual data and this method offered an accuracy of 97.09%.

FENCE to prevent deforestation using an event based sensor network

Research in wireless sensor networks is ongoing at a large scale for applications deployable world-wide like forest monitoring etc. To design, deploy and evaluate novel wireless systems, large and critical application requires a substantial effort. One of the major applications of wireless sensor networks is in event detection. The main objective of this paper is to monitor the forest tree theft and alert using wireless sensor networks. In forest monitoring application, event occurrence is rare, so the communication node is kept in at sleep mode. Whenever an event occurrence has been detected by sensor, it triggers the communication node. Subsequently, the event is reported to the sink node as quickly as possible and an alert is generated. In addition to this, the second objective of this work is power harvesting and power managing that has been achieved using event detection technique.

Uni-model human system interface using sEMG

Todays high-end computer systems contain technologies that only few individuals could have imagined a few years ago. However the conscious input device ergonomics design is still lagging; for example, the extensive usage of computer mouse results in various upper extremity musculoskeletal disorders. This endower towards the developed of HSI system, that act as an alternative or replacement device for computer mouse; thereby one could avoid musculoskeletal disorders. On the other hand, the developed system can also act as an aid tool for individuals with upper extremity disabled. The above issue can be addressed by developing a framework for Human System Interface (HSI). using biological signal as an input signal. The objective of this paper is to develop the framework for HSI system using Surface Electromyogram for individuals with various degrees of upper extremity disabilities. This framework involves the data acquisition of muscle activity, translator algorithm that analysis and translate the EMG as control signal and a platform independent tool to provide mouse cursor control. Thus developed HSI system is validate on applications like web-browsing, simple arithmetic calculation with the help of GUI tool designed.