Biomedical Engineering: Applications, Basis and Communications

The measurement, monitoring of heartbeat and its rate are necessary to know the health of the heart of human beings. In addition, they are needed for extended applications like criminal investigation, law enforcement, defense and military usage, search, rescue operation, etc. The ultra-wideband (UWB) radars found growing interest in recent years as they are able to overcome the limitations of continuous-wave (CW) Doppler radars in detecting human heartbeat. Modeling and analysis of the UWB pulse propagation behavior through a human body is important before developing a practical UWB radar. Several researchers have estimated the reflected signals to study if their variations correlate with the heartbeat rate. However, the reflected signal strength carrying Doppler information received at the radar after a two-way propagation of the RF signal was found to be too weak for detection.

This paper presents (i) a model for UWB wave propagation through a human thorax and (ii) estimation via simulation, of transmission coefficient at various frequencies in the UWB range 1–10GHz using CST Microwave Studio. The study clearly indicates that the variation of power transmission coefficient of UWB signal has a strong correlation to the instantaneous dimension of the heart in a cardiac cycle, a feature that can be exploited in detecting cardiac activity of human being using radar-based principles.

Wireless body sensor network (WBSN) has gained great attention in the environmental and military applications, but security is the major issue, nowadays. In addition, the data exchanged through the wireless sensor network (WSN) is vulnerable to several malicious attacks because of the physical defense equipment needs. Hence, various intrusion detection methods are required for defending against such attacks. Accordingly, an effective method, named deep recurrent neural network (Deep RNN), is proposed in this research for detecting the intrusion in WBSN. At first, the WBSN nodes are utilized to sense the data from the health records of patient for acquiring certain parameters to make risk assessment. Then, WBSN nodes transmit the data to the target nodes using the obtained parameters. After the determination of parameters, the WBSN nodes are responsible to collect the information of the patient and transfer the obtained information to cluster heads (CHs) based on the hybrid harmony search algorithm–particle swarm optimization (HSA–PSO). HSA–PSO is utilized for identifying the optimal CH node iteratively. From the selected CHs, secure communication is done to exchange the data packets. After that, the KDD features are extracted and intrusion detection is done using the proposed Deep RNN. After the genuine users are detected, the classification is done using fractional cat-based salp swarm algorithm (FCSSA) for the risk assessment. The performance of the intrusion detection and health risk assessment in WBSN based on the proposed model is evaluated based on accuracy, sensitivity, and the specificity. The developed model achieves the maximal accuracy of 95.79%, maximal sensitivity of 95.97%, and the maximal specificity of 95.61% using Cleveland dataset.

An iris location method based on mathematical morphology and improved Hough transform is proposed in this paper. When locating the iris inner boundary, an iris inner boundary method based on mathematical morphology and circle fitting is proposed. The iris image is first preprocessed to get a binary image by the gray stretch transform or the circular region mean filter, and then the type of the connected region selected as the iris inner boundary in the binary image is determined by the ratio of the width to the length. On the foundation, the different method is used to extract the parameters of the connected region and the iris inner boundary is obtained. When locating the iris outer boundary, an iris outer boundary method based on the improved Hough transform is put forward. First, the Gaussian filter is used to deal with the iris image, the filtered image is reduced, Canny operator is employed for edge detection, an annular region centered at the center of the iris inner boundary of the reduced image is cropped to explore a circle by Hough transform, and the center and the radius of the iris outer boundary are worked out. The experimental results reveal that this proposed method has the advantages of fast speed, high accuracy, strong robustness and practicability.

The research work has focused on detection and prediction of melanoma which is done by subjecting to features extraction, where the features of an image consisting of melanoma regions are detected by analysis and this analysis is done by considering the features like color and texture-based features learning strategy. These features are extracted by combining color and texture-based features extraction with deep convolutional features representation learning strategy. The colors of images are extracted by representing the colors of different channels into red, green and blue channel information. The combination of texture features extraction with color-based features extraction in addition to Alex net features extraction learning has made the system more robust and efficient toward the segmentation and classification of images. Further, the erected method involves convoluting the features of extracted information with color and texture-based method which has led our system to full convolution neural networks with images features extraction. The melanoma is detected and segmented with watershed segmentation, these segmented features are subjected to the proposed features extraction method, where the features are extracted by combining the methods of texture with color-based information. These colors are made available to the proposed method by analyzing the regions of melanoma images. The erected method does the task of features extraction by Weber law descriptors in combination with red, green, blue channels information extracted from features representation learning. The proposed method has yielded an accuracy of 94.12% of segmentation accuracy and a classification accuracy of 94.32% with respect to various other classification techniques.

Telehealth is the usage of digital medical data and telecommunication technologies to aid long-distance clinical healthcare. Proper maintenance of a patient’s medical record has been a hindrance toward the growth of Telehealth services. The patient’s data, particularly emergency data, must be available to medical personnel within a short time frame and independent of potential interruption of network connections. In this paper, we propose a Near Field Communication (NFC) wristband that provides access to the medical details, history and contact details of a patient. By this, the doctor or the paramedical officer can provide the patient with the best treatment within a short period of time. This system comes in handy even if the patient is unconscious/unable to answer. The doctor or the paramedical officer can quickly draft a patient’s report and send it to the hospital. The proposed framework consists of the following four stages: (i) obtaining emergency medical data with the help of NFC chips, (ii) converting medical report into digital text using Optical Character Recognition (OCR), (iii) structuring the narrative medical text into organized data using NLP and (iv) comparative analysis of the selected content. An image database with 1200 medical case histories has been utilized for the algorithm development and validation. The OCR algorithms for converting images to text produced more than 98% on average and NLP algorithm produced around 94.1% accuracy. Overall, the performance of the system from NFC reader till analysis of the specific field is more than 95%. The developed OCR and NLP integrated software helps the doctor or the health officer to immediately convert it into text format and the unstructured text is quickly organized into the respective fields.

It is very important for the brain study to design a multi-channel and high signal-to-noise ratio (SNR) bio-medical signal capture, record, and analysis system, which can effectively enhance accuracy and precision of the signal capture under dozens to hundreds of microvolts. Unfortunately, the system for data acquisition is very easily interfered by the environment, the power, and the bio-amplifier, so that the results will lead to a failed promotion of capturing high SNR signals, especially in the tiny brain wave signal. In this study, it has been designed an inexpensive, purpose-built, high SNR brainwave signals measurement system. The system is composed of an improved capture system, record system, and analysis system. To better consider the strength and characteristics of the tiny brainwave signals, the system was designed to include a suitable bio-amplifier to make each channel of the invasive microelectrode able to collect the brainwave signals correctly and it provides recording and analysis software, which can not only extract the characteristics of brainwave signals, but also quickly classify signals. The system can collect biological signals from 10$\mu$V to 420$\mu$V and has a high SNR$>$30. The proposed system is easy to make and can be fabricated for the relatively low cost of only US$203. The brain wave signals from the three actions can also be easily classified, with a correct rate up to 46.70%. The system has six improvements: good SNR, the ability to capture small signals, modularity, a low price, easy fabrication, and simple operation.

This study aimed to determine the effectiveness of using noninvasive arterial pulse-wave and laser-Doppler flowmetry (LDF) measurements to discriminate between colorectal-cancer (CC) patients and healthy control subjects. Radial-artery blood pressure waveform (BPW), finger photoplethysmography (PPG), and skin-surface LDF signals were measured noninvasively in 12 CC patients and 25 control subjects. Beat-to-beat, spectral, and variability analyses were applied to 20-minute-long recorded signals. Significant intergroup differences were found. In BPW, $C_1$–$C_4$ amplitude indices were significantly larger while $P_2$–$P_8$ phase-angle indices were significantly smaller in the CC patients than in the controls. The PPG and LDF variability indices were significantly larger and smaller, respectively, in CC patients. The relative energy contributions of the endothelial-, neural-, and myogenic-related frequency bands in LDF were significantly smaller in CC patients. The present findings indicate that pulse and LDF waveform analysis can be used to evaluate the arterial pulse-wave transmission condition, the responses of the blood-flow perfusion, and its regulatory activities in CC patients. There could be some similarities and differences in the present indices for different types of cancer. These findings could be utilized in the development of a rapid, noninvasive, and objective technique for evaluating the CC-induced blood-flow responses.

The variations in the intervals of successive heartbeats, i.e. between the two R peaks are known as Heart Rate Variability (HRV). HRV is used for identifying the physiological and pathological cardiac functioning of the heart. The characteristics of HRV are changing continuously in the various physical activities such as a change in postures from supine to standing. Therefore, the main objective of this paper is to investigate the autonomic regulations of cardiac activity in the lying and standing postures. The HRV signals of 20 young healthy women were measured during supine and standing postures using BIOPAC${}^{Ⓡ}$ MP150 system. The frequency-domain methods using the Autoregressive (AR) Model and nonlinear methods such as Sample Entropy (SampEn) and Approximate Entropy (ApEn) were used to calculate the posture-related differences in HRV. The Box and Whisker plots are used for representing the distribution of frequency-domain methods and nonlinear methods. Further, paired t-tests were used for validating the statistically significant values. It is concluded from the results that the heart rate is significantly higher and HRV is lower in the standing posture ($p<0.05$) as compared to the supine posture. Lower HRV in the standing posture implies that sympathetic activities were higher and parasympathetic activities were lower in comparison to the supine postures.