International Journal of Biomedical Engineering and Technology 

About: International Journal of Biomedical Engineering and Technology is an academic journal published by Inderscience Publishers. The journal publishes majorly in the area(s): Computer science & Medicine. It has an ISSN identifier of 1752-6418. Over the lifetime, 791 publications have been published receiving 4019 citations. The journal is also known as: Biomedical engineering and technology & IJBMT.

Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction

Abstract: Electrocardiogram (ECG) signal is significant to diagnose cardiac arrhythmia among various biological signals. The accurate analysis of noisy electrocardiographic (ECG) signal is a very motivating challenge. According to this automated analysis, the noises present in electrocardiogram signal need to be removed for perfect diagnosis. Numerous investigators have been reported different techniques for denoising the electrocardiographic signal in recent years. In this paper, an efficient scheme for denoising electrocardiogram (ECG) signals is proposed based on a wavelet-based threshold mechanism. This scheme is based on an opposition-based self-adaptive learning particle swarm optimisation (OSLPSO) in dual tree complex wavelet packet scheme, in which the OSLPSO is utilised to for threshold optimisation. Different abnormal and normal electrocardiographic signals are tested to evaluate this approach from MIT/BIH arrhythmia database, by artificially adding white Gaussian noises with variation of 5 dB, 10 dB and 15 dB. Simulation results illustrate that the proposed system has good performance in various noise level and obtains better visual quality compared with other methods.

A survey of big data architectures and machine learning algorithms in healthcare

Abstract: Big Data has gained much attention from researchers in healthcare, bioinformatics, and information sciences. As a result, data production at this stage will be 44 times greater than that in 2009. Hence, the volume, velocity, and variety of data rapidly increase. Hence, it is difficult to store, process and visualise this huge data using traditional technologies. Many organisations such as Twitter, LinkedIn, and Facebook are used big data for different use cases in the social networking domain. Also, implementations of such architectures of the use cases have been published worldwide. However, a conceptual architecture for specific big data application has been limited. The intention of this paper is application-oriented architecture for big data systems, which is based on a study of published big data architectures for specific use cases. This paper also provides an overview of the state-of-the-art machine learning algorithms for processing big data in healthcare and other applications.

Sleep staging from Heart Rate Variability: time-varying spectral features and Hidden Markov Models

Abstract: An alternative DSS which models the behaviour of the Heart Rate Variability (HRV) signal linked to stable (NREM) and instable (REM) cerebral waves during sleep and a probabilistic model of the sleep stages transitions for decision was developed. Time-Varying Autoregressive Models (TVAMs) were used as feature extractor while Hidden Markov Models (HMM) was used as time series classifier. 24 full polysomnography recordings from healthy sleepers were used for the analysis and those were separated in two sets of

Region-based seed point cell segmentation and detection for biomedical image analysis

Abstract: Salient region detection and segmentation from biological images is often a crucial step for image understanding. The initial contour selection during segmentation being a competent task and wrong differentiation between the foreground and background colours are compromised. In this paper, improved cell detection is introduced by using a region-based cell detection and segmentation method called Histogram Colour Contrast Seed Point Selection (HCC-SPS). In each pixel, the HCC model is able to group similar colour values, therefore addressing colour contrast in visual signal, resulting in accurate desired edge points. Second, considering the energy function, region-based seed point fine tunes the salient value and makes differentiation between salient and background points easier. Third, due to salient mapping function with pixel representation, the segmentation of biological images, done accurately. The results are compared with the existing system based on the parameters such as accuracy rate, segmentation time and mapping functions.

Experiences in the development of a Smart Lab

Abstract: There is now a growing demand to provide improved delivery of health and social care due to changes in the age profile of our population. One area where these services may be improved is through the development of smart living environments. Within this paper we provide an overview of the drivers behind the development of such environments along with details of the different ways in which they may exist. Finally, we provide details of our initial experiences in the establishment of a Smart Living Environment for the development of assistive technologies to support independent living.