Chung Kit Wu

Cardiovascular diseases identification using electrocardiogram health identifier based on multiple criteria decision making

Binary classifier (BC) and multi-class classifier (MCC) are designed and analyzed.A scheme namely MCC-BC will pave the way for speedy and accurate ECGHI.A confidence index is newly proposed to evaluate the performance of ECGHI.MCDM using AHP has been developed to evaluate the optimal hyperplanes of ECGHI. Cardiovascular diseases can wreak havoc on human beings and lead to 30% of global death annually. The World Health Organization has always highlighted that there is a severe shortage of medical personnel, especially cardiologists, in most of the countries. In this paper, an electrocardiogram health identifier (ECGHI) has been proposed and developed for swift identification of heart diseases. The ECGHI has been applied to four most common types of cardiovascular diseases, namely Myocardial Infarction, Dysrhythmia, Bundle Branch Block and Heart Failure since these four types of cardiovascular diseases contribute to 25% of the overall population suffering from heart diseases. In the investigation of ECGHI, the binary classifier (BC) and multi-class classifier (MCC) are designed and analyzed. The MCC features a multi-class support vector machine (SVM) to diagnose the exact type of cardiovascular disease. The BC features a two-class SVM to identify healthiness of heart accurately. In this paper, the following indicators have been investigated, namely the overall accuracy, specificity, sensitivity, the dimensionality of feature vector, the total training and testing time of ECGHI and a newly defined confidence index. These six criteria form the basis to derive an analytic hierarchy process (AHP) to facilitate the multiple criteria decision making (MCDM) for the optimal evaluation of hyperplanes. Four kernels have been analyzed from which both the BC and MCC are evaluated and analyzed. The optimized ECGHI using BC yields an AHP Performance Score of 0.079 with score components (overall accuracy, specificity, sensitivity, average confidence index, dimensionality, total time for training and testing time) of 0.982, 0.978, 0.986, 0.608, 6, and 5.77s respectively. Likewise, the optimized ECGHI using MCC yields an AHP Performance Score of 0.093 with score components of 0.882, 0.89, 0.874, 0.504, 9, and 7.32s respectively. The BC is employed as a supplement of the MCC to achieve a further improvement in all six criteria. Such a novel process of identification and detection with high accuracy is referred as the MCC-BC scheme. The developed ECGHI (MCC) may identify the FOUR most common and important cardiovascular diseases simultaneously (with BC supplementing the MCC to achieve a high accuracy). Such simultaneous identification of cardiovascular diseases is the first of its kind in this research area, so no comparison can be made. The MCC-BC scheme will pave the way for speedy and accurate identification and detection of heart disease. The instant response of the ECGHI minimizes the probability of death from Myocardial Infarction, Bundle Branch Block, Dysrhythmia, and Heart Failure.

A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm

A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day.

An Accurate ECG-Based Transportation Safety Drowsiness Detection Scheme

Many traffic injuries and deaths are caused by the drowsiness of drivers during driving. Existing drowsiness detection schemes are not accurate due to various reasons. To resolve this problem, an accurate driver drowsiness classifier (DDC) has been developed using an electrocardiogram genetic algorithm-based support vector machine (ECG GA-SVM). In existing studies, a cross correlation kernel and a convolution kernel have both been applied for performing the classification. The DDC is designed by a Mercer kernel KDDC formed by commuting the cross correlation kernel Kxcorr,ij and the convolution kernel Kconv,ij. Kxcorr,ij, and captures the symmetric information among ECG signals from different classes, while Kconv,ij captures the antisymmetric information among ECG signals from the same class. The final KDDC (a precomputed kernel) is obtained by a genetic mutation using a multiobjective genetic algorithm. This renders an optimal KDDC that confidently serves as the full descriptor of the drowsiness. The performance of KDDC is compared with the most prevailing kernels. The obtained DDC yields an overall accuracy of 97.01%, sensitivity of 97.16%, and specificity of 96.86%. The analysis reveals that the accuracy of KDDC is better than those of both Kxcorr,ij and Kconv,ij by more than 11%, and typical kernels including linear, quadratic, third order polynomial, and Gaussian radial basis function by 17-63%, respectively. Comparing with related works using the image-based method and the biometric signal-based method, KDDC improves the accuracy by 48.4-87.2%. Testing results showed that KDDC has a less than 1% deviation from simulated results. Also, the average delay of DDC was bounded by 0.55 ms. This renders the real time implementation. Thus, the developed ECG GA-SVM provides an accurate and instantaneous warning to the drivers before they fall into sleep. As a result this ensures the public transport safety.

ZigBee based wireless sensor network in smart metering

A new network applies on high traffic ZigBee based wireless network. The new network incorporates multi-radio multi-channel technology and improves the efficiency of data transmission. The latency performance of the proposed network is analyzed by OPNET.

Electrocardiogram based classifier for driver drowsiness detection

Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy, sensitivity, and specificity of 76.93%, 77.36%, and 76.5% respectively. Results have revealed that the performance of proposed classifier is better than traditional methods.

A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram

Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost

Traffic condition monitoring using weighted kernel density for intelligent transportation

500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human’s biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.

An improved drowsiness detection scheme

Smart transportation is an application of intelligent system on transportation domain, expected to bring the society environmental and economic advantages. By combining with IoT techniques, the concept is being enhanced and raised to a system level. Numerous data are able to collect and effective analysis technique is needed. Here in this paper, we proposed a framework of employing IoT technique to construct a free time navigation system. The system aims at providing a real-time quantification of traffic conditions and suggests optimal route based on the information retrieved. The system can be basically separated into two major components: (i) the traffic condition estimation module and the (ii) real-time routing algorithm. In the first component, traffic conditions of roads will be estimated based the information collected from sensors installed on vehicles. Based on these location and speed information, the traffic condition can be quantified using a weighted kernel density estimation (WKDE) function. This function is a function of time and provides a real time insight of the overall traffic condition. By combining this information and the topological structure of the road network, a more accurate time consumption on each road can be estimated and hence enable a better routing.

Detecting Parkinson's diseases via the characteristics of the intrinsic mode functions of filtered electromyograms

Worldwide, more than 50 million people are injured in each year because of traffic accidents and their expenditure costs 1% to 3% of the worlds GDP. Drowsy driver is one of the leading causes of the traffic accidents. To reduce the accidents, drowsy driver detection (DDD) using penalized cross-correlation kernel (KPC) has been developed. The cross-correlation is to measure the similarity of ECG signals and this phenomenon is reflected in the resultant cross-correlation coefficients. The proposed work with KPC is regarded as a more accurate algorithm for driver drowsiness detection.

Efficiency and robustness management for IEEE 802.15.4 in healthcare sensor network

This paper proposes a novel method for detecting the Parkinsons diseases via applying the empirical mode decomposition to filtered electromyograms. First, the electromyograms are processed by different linear phase finite impulse response bandpass filters with different pairs of cutoff frequencies. Second, each filtered electromyogram is decomposed into several intrinsic mode functions. Third, both the entropies and the total numbers of the extrema of the intrinsic mode functions of each filtered electromyogram are computed and they are used as the features for detecting the Parkinsons diseases. Computer numerical simulation results show that the features are linearly separable. Hence, a simple perceptron can be employed for the detection of the Parkinsons diseases. Finally, the algorithm is implemented via a mobile application. Compared to conventional empirical mode decomposition approaches in which a predefined number of features is employed for detecting the Parkinsons diseases, our proposed method allows to use a flexible number of features for detecting the Parkinsons diseases. This is because the total number of filters to be employed is very flexible. As a result, our proposed method is more flexible than the existing methods.