Tan Tian Swee

Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals

Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart abnormalities. Due to the presence of noise, the non-stationary nature of the ECG signal (i.e. the changing morphology of the ECG signal with respect to time) and the irregularity of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. The computer-aided analysis of ECG results assists physicians to detect cardiovascular diseases. The development of many existing arrhythmia systems has depended on the findings from linear experiments on ECG data which achieve high performance on noise-free data. However, nonlinear experiments characterize the ECG signal more effectively sense, extract hidden information in the ECG signal, and achieve good performance under noisy conditions. This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data. In this study, five types of beat classes of arrhythmia as recommended by the Association for Advancement of Medical Instrumentation are analyzed: non-ectopic beats (N), supra-ventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F) and unclassifiable and paced beats (U). The characterization ability of nonlinear features such as high order statistics and cumulants and nonlinear feature reduction methods such as independent component analysis are combined with linear features, namely, the principal component analysis of discrete wavelet transform coefficients. The features are tested for their ability to differentiate different classes of data using different classifiers, namely, the support vector machine and neural network methods with tenfold cross-validation. Our proposed method is able to classify the N, S, V, F and U arrhythmia classes with high accuracy (98.91%) using a combined support vector machine and radial basis function method.

Wireless data gloves Malay sign language recognition system

This paper describes the structure and algorithm of the whole Wireless Bluetooth Data Gloves Sign Language Recognition System, which is defined as a Human-Computer Interaction (HCI) system. This project is based on the need of developing an electronic device that can translate sign language into speech (sound) in order to make the communication take place between the mute & deaf community with the general public possible. Hence, the main objective of this project is to develop a system that can convert sign language into speech so that deaf people are able to communicate efficiently with normal people. This Human-Computer Interaction system is able to recognize 25 common words signing in Bahasa Isyarat Malaysia (BIM) by using Hidden Markov Models (HMM) methods. Both hands are involved in performing the BIM with all the sensor connecting wirelessly to PC with Bluetooth module. In the future, the system can be shrunk to become a stand alone system without any interaction with PC.

Multipurpose contrast enhancement on epiphyseal plates and ossification centers for bone age assessment

BackgroundThe high variations of background luminance, low contrast and excessively enhanced contrast of hand bone radiograph often impede the bone age assessment rating system in evaluating the degree of epiphyseal plates and ossification centers development. The Global Histogram equalization (GHE) has been the most frequently adopted image contrast enhancement technique but the performance is not satisfying. A brightness and detail preserving histogram equalization method with good contrast enhancement effect has been a goal of much recent research in histogram equalization. Nevertheless, producing a well-balanced histogram equalized radiograph in terms of its brightness preservation, detail preservation and contrast enhancement is deemed to be a daunting task.MethodIn this paper, we propose a novel framework of histogram equalization with the aim of taking several desirable properties into account, namely the Multipurpose Beta Optimized Bi-Histogram Equalization (MBOBHE). This method performs the histogram optimization separately in both sub-histograms after the segmentation of histogram using an optimized separating point determined based on the regularization function constituted by three components. The result is then assessed by the qualitative and quantitative analysis to evaluate the essential aspects of histogram equalized image using a total of 160 hand radiographs that are implemented in testing and analyses which are acquired from hand bone online database.ResultFrom the qualitative analysis, we found that basic bi-histogram equalizations are not capable of displaying the small features in image due to incorrect selection of separating point by focusing on only certain metric without considering the contrast enhancement and detail preservation. From the quantitative analysis, we found that MBOBHE correlates well with human visual perception, and this improvement shortens the evaluation time taken by inspector in assessing the bone age.ConclusionsThe proposed MBOBHE outperforms other existing methods regarding comprehensive performance of histogram equalization. All the features which are pertinent to bone age assessment are more protruding relative to other methods; this has shorten the required evaluation time in manual bone age assessment using TW method. While the accuracy remains unaffected or slightly better than using unprocessed original image. The holistic properties in terms of brightness preservation, detail preservation and contrast enhancement are simultaneous taken into consideration and thus the visual effect is contributive to manual inspection.

Medical image visual appearance improvement using bihistogram Bezier curve contrast enhancement: data from the Osteoarthritis Initiative.

Well-defined image can assist user to identify region of interest during segmentation. However, complex medical image is usually characterized by poor tissue contrast and low background luminance. The contrast improvement can lift image visual quality, but the fundamental contrast enhancement methods often overlook the sudden jump problem. In this work, the proposed bihistogram Bezier curve contrast enhancement introduces the concept of “adequate contrast enhancement” to overcome sudden jump problem in knee magnetic resonance image. Since every image produces its own intensity distribution, the adequate contrast enhancement checks on the images maximum intensity distortion and uses intensity discrepancy reduction to generate Bezier transform curve. The proposed method improves tissue contrast and preserves pertinent knee features without compromising natural image appearance. Besides, statistical results from Fishers Least Significant Difference test and the Duncan test have consistently indicated that the proposed method outperforms fundamental contrast enhancement methods to exalt image visual quality. As the study is limited to relatively small image database, future works will include a larger dataset with osteoarthritic images to assess the clinical effectiveness of the proposed method to facilitate the image inspection.

An artifacts removal post-processing for epiphyseal region-of-interest (EROI) localization in automated bone age assessment (BAA)

BackgroundSegmentation is the most crucial part in the computer-aided bone age assessment. A well-known type of segmentation performed in the system is adaptive segmentation. While providing better result than global thresholding method, the adaptive segmentation produces a lot of unwanted noise that could affect the latter process of epiphysis extraction.MethodsA proposed method with anisotropic diffusion as pre-processing and a novel Bounded Area Elimination (BAE) post-processing algorithm to improve the algorithm of ossification site localization technique are designed with the intent of improving the adaptive segmentation result and the region-of interest (ROI) localization accuracy.ResultsThe results are then evaluated by quantitative analysis and qualitative analysis using texture feature evaluation. The result indicates that the image homogeneity after anisotropic diffusion has improved averagely on each age group for 17.59%. Results of experiments showed that the smoothness has been improved averagely 35% after BAE algorithm and the improvement of ROI localization has improved for averagely 8.19%. The MSSIM has improved averagely 10.49% after performing the BAE algorithm on the adaptive segmented hand radiograph.ConclusionsThe result indicated that hand radiographs which have undergone anisotropic diffusion have greatly reduced the noise in the segmented image and the result as well indicated that the BAE algorithm proposed is capable of removing the artifacts generated in adaptive segmentation.

Design and development of speech-control robotic manipulator arm

This paper presents a speech-control robotic manipulator arm via personal computer and self-built robotic arm. It describes the software we developed to collect, process and train the speech database. First, the specifications of the robotic arm will be explained. The algorithms involved will also be briefly discussed. Finally, the result will show the ability of this system to control robotic manipulator arm with humans voice.

Speech pitch detection using short-time energy

Research on pitch detection for speech has been done and still ongoing since there is not one algorithm found that perfectly detects the pitch. This paper will show the sum of square energy method in detecting the voiced/unvoiced speech since they have different level of energy. The threshold was found through experiments to find the unvoiced energy level from candidates with various words consist of unvoiced phonemes. Pitch detection algorithm was then implemented and the percentage of pitch detected in words was evaluated to test the accuracy of the algorithm proposed in this paper.

Malay Sign Language Gesture Recognition system

This paper describes hardware design and sensors setting and configuration for Malay sign language gesture recognition systems. A set of sensors consists of accelerometers and flexure sensors has been setup to capture the movement or gesture of shoulder, elbow, wrist, palm and fingers. This project is based on the need of developing an electronic device that can translate the sign language into speech (sound) in order to enable communication to take place between the mute and deaf community with the common public. Hence, the main objective of this project is to develop a system that can convert the sign language into speech so that deaf people are able to communicate efficiently with normal people. This Human-Computer Interaction system is able to recognize 25 common words signing in Bahasa Isyarat Malaysia (BIM) by using the Hidden Markov Models (HMM) method. Both hands are involved to perform the BIM with all the sensors connect wirelessly to a PC with a Bluetooth module. This project aims to capture the hand gestures which involve multiple axis of movement. Altogether 24 sensors have been setup in different hand locations to capture hand and wrist movement in different directions.

Performance metrics for active contour models in image segmentation

Image segmentation is one of the significant techniques in image processing to distinguish desired parts from its background for further analysis. It provides visual means for inspection of anatomical structure of human body, identification of disease, tracking of its development and input for surgical planning and simulation. Active contour models are regarded as promising and vigorously research model-based approach to computer assisted medical image analysis. However, it is not trivial to assess whether one segmentation algorithm performs more superior than the other. Therefore, a systematic assessment tool is designed and implemented to examine all the important aspects of active contour models. Meanwhile, a novel supervised evaluator including analytical method and empirical methods are proposed to acts as objective evaluator. The obtained results highlighted both the strengths and limitations of the studied active contour models. A proper area usage of each active contour model is also suggested at the end of this paper.

Low footprint high intelligibility malay speech synthesizer based on statistical data

Speech synthesis plays a pivotal role nowadays. It can be found in various daily applications such as in mobile phones, navigation systems, languages learning software and so on. In this study, a Malay language speech synthesizer was designed using hidden Markov model to improve the performance of current Malay speech synthesizer and also extend Malay speech technology. Statistical parametric method was utilized in this study. The database was constructed to be balanced with all the phonetic sample appeared in Malay language. The results were rated by 48 listeners and obtained a moderate high rating ranging from 3.79 to 4.23 out of 5. The computed Word Error Rate is 7.1%. The total file size is less than 2 Megabytes which means it is suitable to be embedded into daily application. In conclusion, a Malay language speech synthesizer was designed using statistical parametric method with hidden Markov model. The output speech was verified to be good in quality. The file size is small indicates the feasibility to be used in embedded system.