Faan Hei Hung

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.

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.

A speedy cardiovascular diseases classifier using multiple criteria decision analysis.

Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA) to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented.

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

An improved drowsiness detection scheme

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.

Efficiency and robustness management for IEEE 802.15.4 in healthcare sensor network

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.

A 2.4 GHz CMOS power amplifier

To meet the requirements on data collection from wireless sensor network for healthcare application especially in hospital, the performance of wireless communication is an important issue. In particular, simultaneous transmissions from numerous sensors will cause serious collision which leads to transmission packets loss and delay. In this paper, a management scheme, multi-criteria decision making method using TOPSIS, for IEEE 802.15.4 is proposed for wireless sensor network. The performance of the network is determined by beacon order, superframe order, contention window, number of backoffs and backoff exponent. By analyzing slotted CSMA-CA mechanism, which is in the beacon-enabled mode, through OPNET, the proposed scheme can estimate the best combination of the parameters. The results show that the proposed scheme achieves the best combination of low end-to-end delay, high throughput and high successful probability.

RSS-based localization algorithm for indoor patient tracking

A CMOS amplifier at 2.4GHz has been implemented. The developed power amplifier has a compact size of 1.3×0.9 mm2. The power amplifier features 12 dB gain at 2.4 GHz frequency band.

ZigBee LNA design for wearable healthcare application

The application of localization in healthcare system is a crucial topic which helps to locate the position of patent or the elderly in case urgency happens. From this aspect, a wireless technology is adopted to provide an efficient localization monitoring system for patients or the elderly in indoor area. The location of patients can be obtained through the developed algorithm. Fuzzy C-Means clustering (FCM) is one of the applicable techniques to locate the position of patients. However, low accuracy of FCM is the main problem. For this reason, the revised FCM localization algorithm, Calibrated Fuzzy C-Means Clustering Algorithm (C-FCM) is proposed in this investigation based on received signal strength (RSS) in wearable device. The proposed algorithm is evaluated through experiment and it has a percentage improvement of 14% compared with FCM.