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Phonocardiogram (PCG) signals are contaminated with various noise signals, which hinders the accurate diagnostic interpretation of the signal. Discrete wavelet transform (DWT) is a well-known technique used to remove noise from PCG signals and improve signal quality. The performance of DWT-based denoising depends upon several parameters involved in the process, such as mother wavelets used for decomposition, the number of decomposition levels (DLs), thresholding technique used and the threshold estimation rule followed. In this work, an investigative study is carried out to select the optimal parameter values which give the best denoising performance. The metrics such as mean-square error (MSE), normalized-mean-square error (NMSE), root-mean-square error (RMSE), percentage root-mean-square difference (PRD) and signal-to-noise ratio (SNR) are used to evaluate the performance of the denoising in this study. The results obtained show that the fifth-order Coiflet wavelet is best suited for denoising PCG signals when applied with the soft thresholding (ST) function and rigrsure threshold selection rule. Also, the optimum number of DLs resulting in better performance is level 6. SNR value obtained from the studies shows the efficacy of the parameters selected for denoising. The denoised PCG signals provide accurate information to determine various kinds of heart valve-related disorders (HVDs).

In this letter, a transform-based methodology is presented for the compression of fetal phonocardiogram (fPCG) signal. Transform techniques, such as discrete cosine transform (DCT), discrete wavelet transform (DWT), and fast Walsh–Hadamard transform (FWHT), are applied for the decomposition of fPCG signal and a comparative study on the performance of different transforms is made in terms of measures, such as compression ratio (CPR), percent root-mean-square difference (PRD), mean square error (MSE), quality score (QS), and signal-to-noise ratio (SNR). The simulation results reveal the effectiveness of the transform techniques in fPCG signal compression. In comparison, it is observed that FWHT gives a better CPR, whereas DWT yields good fidelity parameters with comparable CPR.

In this letter, we propose a method for the automated detection of heart valve disorders namely, the aortic stenosis (AS), mitral stenosis (MS), and mitral regurgitation (MR) from the phonocardiogram (PCG) signal. The wavelet synchrosqueezing transform is used to obtain the time-frequency matrix from the segmented cycles of the PCG signal. From the time-frequency matrix, the magnitude and phase features are extracted. The random forest (RF) classifier is used for the classification. The results reveal that the proposed method has the average individual accuracy (IA) values of 98.83%, 97.66%, 91.16%, and 92.83% for normal, AS, MS, and MR classes.

The automated classification of cognitive workload tasks based on the analysis of multi-channel EEG signals is vital for human–computer interface (HCI) applications. In this paper, we propose a computerized approach for categorizing mental-arithmetic-based cognitive workload tasks using multi-channel electroencephalogram (EEG) signals. The approach evaluates various entropy features, such as the approximation entropy, sample entropy, permutation entropy, dispersion entropy, and slope entropy, from each channel of the EEG signal. These features were fed to various recurrent neural network (RNN) models, such as long-short term memory (LSTM), bidirectional LSTM (BLSTM), and gated recurrent unit (GRU), for the automated classification of mental-arithmetic-based cognitive workload tasks. Two cognitive workload classification strategies (bad mental arithmetic calculation (BMAC) vs. good mental arithmetic calculation (GMAC), and before mental arithmetic calculation (BFMAC) vs. during mental arithmetic calculation (DMAC)) are considered in this work. The approach was evaluated using the publicly available mental arithmetic task-based EEG database. The results reveal that our proposed approach obtained classification accuracy values of 99.81%, 99.43%, and 99.81%, using the LSTM, BLSTM, and GRU-based RNN classifiers, respectively for the BMAC vs. GMAC cognitive workload classification strategy using all entropy features and a 10-fold cross-validation (CV) technique. The slope entropy features combined with each RNN-based model obtained higher classification accuracy compared with other entropy features for the classification of the BMAC vs. GMAC task. We obtained the average classification accuracy values of 99.39%, 99.44%, and 99.63% for the classification of the BFMAC vs. DMAC tasks, using the LSTM, BLSTM, and GRU classifiers with all entropy features and a hold-out CV scheme. Our developed automated mental arithmetic task system is ready to be tested with more databases for real-world applications.

In this paper, we discussed the parametric optimization of improved sensing schemes in multi-antenna cognitive radio (CR) networks over an erroneous channel. The CR utilizes an improved energy detector (IED) by making the binary decision statistics of the absence or presence of a primary user. From primary users’ statistics, the improved energy detector measures power with respect to the threshold so as to make correct spectrum decisions. This decision is further transferred to the fusion center (FC) by the erroneous channel, which decides the complete decision of the existence of PU. For the detection of the spectrum hole and to obtain an optimized number of CRs, we minimize the total error rate (TER) in cooperative spectrum sensing. An optimized value of sensing threshold and arbitrary positive power (p) of each CR is also accessed by considering the TER. The numerical-based simulation results show the validation of correct sensing performance.

Phonocardiogram (PCG) signal contains significant bio-acoustic information reflecting the operation of the heart, and is used to detect the various diseases related to heart valve. But it is highly susceptible to noise, and the sources of noise includes lung and breath sounds, noise from contact between the recording device and skin, environmental noise, etc. Hence, denoising of PCG signal is very important for the proper diagnosis of heart diseases. In this paper, a discrete wavelet transform (DWT) based threshold tuning is investigated to deliver denoised PCG signal. The performance of the denoising algorithm is evaluated using different metrics such as mean-square error, normalized-mean-square error, root-mean-square error, percentage root-mean-square difference, and signal-to-noise ratio by determining the most suitable parameters (wavelet family, level of decomposition, and thresholding type) for the denoising process. The evaluation results obtained from the different metrics gives the best denoising performance from the reconstructed PCG signal.

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes.

Heart valve diseases (HVDs) are a group of cardiovascular abnormalities, and the causes of HVDs are blood clots, congestive heart failure, stroke, and sudden cardiac death, if not treated timely. Hence, the detection of HVDs at the initial stage is very important in cardiovascular engineering to reduce the mortality rate. In this article, we propose a new approach for the detection of HVDs using phonocardiogram (PCG) signals. The approach uses the Chirplet transform (CT) for the time-frequency (TF) based analysis of the PCG signal. The local energy (LEN) and local entropy (LENT) features are evaluated from the TF matrix of the PCG signal. The multiclass composite classifier formulated based on the sparse representation of the test PCG instance for each class and the distances from the nearest neighbor PCG instances are used for the classification of HVDs such as mitral regurgitation (MR), mitral stenosis (MS), aortic stenosis (AS), and healthy classes (HC). The experimental results show that the proposed approach has sensitivity values of 99.44%, 98.66%, and 96.22% respectively for AS, MS and MR classes. The classification results of the proposed CT based features are compared with existing approaches for the automated classification of HVDs. The proposed approach has obtained the highest overall accuracy as compared to existing methods using the same database. The approach can be considered for the automated detection of HVDs with the Internet of Medical Things (IOMT) applications.

In this article, we discussed IoT-based children activity tracking system through which we can ensure the safety of the child. In this research, we implement the device which will be present with the child consisting of Global System for Mobile communications (GSM) and Global Positioning System (GPS) modules through which we know the text messages about the situation of the child and it also gives the exact location of the child in Google maps. The device consists of different types of sensors like temperature, light dependent resistor (LDR) and micro-electro-mechanical sensor (MEMS). If the temperature and sun light around the child increases more than the limit, then it automatically sends the text message to the parents and the buzzer will also be activated. Buzzer is used to indicate the bystanders if the child is in any danger conditions. MEMS accelerometer sensors are used to detect the tilt varies directions. The voice module is used to record the voice and sends the information automatically when the child falls down. Even it is also possible to track the child location at anytime, anywhere by sending one text message to the GSM module present with the child. The device is portable, and the child can wear it for secure purpose.

The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs.

Heart sound or phonocardiogram (PCG) signal quantifies the information about the mechanical activity of the heart, and the medical practitioners use the stethoscope to listen to this sound. The PCG signal can be used for clinical applications such as detection of various valvular diseases and non-clinical applications such as biometric system, stress and emotion detection, etc. The PCG signal acquisition and preprocessing are important tasks for the diagnosis of heart valve-related disorders and other applications. The heart sound preprocessing techniques include denoising of PCG signal, segmentation of first and second heart sound (S1, S2) and other heart sound components from the PCG signal, feature extraction from the segmented heart sound components, followed by classification. This chapter reviews the state-of-the-art approaches for heart sound acquisition and pre-processing techniques and also provides the information that is commonly used by the researchers for the validation of their PCG signal processing algorithms.

In order to increase the spectral efficiency of any communication systems, spectrum sensing techniques may be used for proficient utilization of inadequate spectrum resources. It identifies the unused spectrum holes, which is originally assigned to the primary users (PU). These spectrum holes are then assigned to the secondary or cognitive users with avoiding interference to the primary users. In this paper, a spectrum assignment technique based on energy detection technique is proposed. This enhanced energy detection technique works well at low signal-to-noise ratio (SNR), which makes the communication system more power efficient and can be for low power applications. Further, the performance of the proposed spectrum sensing method is examined for cognitive radio (CR) network. The performance of the proposed method is also examined by calculating the probability of detection, probability of false alarm and error probability in presence of additive Gaussian noise and the effect of different sensing parameters on the probability of error in detecting primary users are also evaluated.

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is characterized by the heart's beating in an uncoordinated manner. In clinical studies, patients often do not have visible symptoms during AF, and hence it is harder to detect this cardiac ailment. Therefore, automated detection of AF using the electrocardiogram (ECG) signals can reduce the risk of stroke, coronary artery disease, and other cardiovascular complications. In this paper, a novel time-frequency domain deep learning-based approach is proposed to detect AF and classify terminating and non-terminating AF episodes using ECG signals. This approach involves evaluating the time-frequency representation (TFR) of ECG signals using the chirplet transform. The two-dimensional (2D) deep convolutional bidirectional long short-term memory (BLSTM) neural network model is used to detect and classify AF episodes using the time-frequency images of ECG signals. The proposed TFR based 2D deep learning approach is evaluated using the ECG signals from three public databases. Our developed approach has obtained an accuracy, sensitivity, and specificity of 99.18% (Confidence interval (CI) as [98.86, 99.49]), 99.17% (CI as [98.85 99.49]), and 99.18% (CI as [98.86 99.49]), respectively, with 10-fold cross-validation (CV) technique to detect AF automatically. The proposed approach also classified terminating and non-terminating AF episodes with an average accuracy of 75.86%. The average accuracy value obtained using the proposed approach is higher than the short-time Fourier transform (STFT), discrete-time continuous wavelet transform (DT-CWT), and Stockwell transform (ST) based time-frequency analysis methods with deep convolutional BLSTM models to detect AF. The proposed approach has better AF detection performance than the existing deep learning-based techniques using ECG signals from the MIT-BIH database.

Emotion is interpreted as a psycho-physiological process, and it is associated with personality, behavior, motivation, and character of a person. The objective of affective computing is to recognize different types of emotions for human-computer interaction (HCI) applications. The spatiotemporal brain electrical activity is measured using multi-channel electroencephalogram (EEG) signals. Automated emotion recognition using multi-channel EEG signals is an exciting research topic in cognitive neuroscience and affective computing. This paper proposes the rhythm-specific multi-channel convolutional neural network (CNN) based approach for automated emotion recognition using multi-channel EEG signals. The delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) rhythms of EEG signal for each channel are evaluated using band-pass filters. The EEG rhythms from the selected channels coupled with deep CNN are used for emotion classification tasks such as low-valence (LV) vs. high valence (HV), low-arousal (LA) vs. high-arousal (HA), and low-dominance (LD) vs. high dominance (HD) respectively. The deep CNN architecture considered in the proposed work has eight convolutions, three average pooling, four batch-normalization, three spatial drop-outs, two drop-outs, one global average pooling and, three dense layers. We have validated our developed model using three publicly available databases: DEAP, DREAMER, and DASPS. The results reveal that the proposed multivariate deep CNN approach coupled with β-rhythm has obtained the accuracy values of 98.91%, 98.45%, and 98.69% for LV vs. HV, LA vs. HA, and LD vs. HD emotion classification strategies, respectively using DEAP database with 10-fold cross-validation (CV) scheme. Similarly, the accuracy values of 98.56%, 98.82%, and 98.99% are obtained for LV vs. HV, LA vs. HA, and LD vs. HD classification schemes, respectively, using deep CNN and θ-rhythm. The proposed multi-channel rhythm-specific deep CNN classification model has obtained the average accuracy value of 57.14% using α-rhythm and trial-specific CV using DASPS database. Moreover, for 8-quadrant based emotion classification strategy, the deep CNN based classifier has obtained an overall accuracy value of 24.37% using γ-rhythms of multi-channel EEG signals. Our developed deep CNN model can be used for real-time automated emotion recognition applications.

The present study was undertaken to know the anatomical basis of medial sural artery (MSA) and its perforators in Nepalese. The popliteal arteries of 16 preserved cadaveric lower limbs were injected with a mixture of red ink and glycerine. The number, location, diameter of perforators; length and intramuscular course of pedicle; the branching pattern of MSA were observed and measured. The mean of 2.2 ± 1.2 perforators (range 0–4) was observed. The perforators were clustered between 8.6 and 25.7 cm from the popliteal crease and 0.3–7.5 cm from posterior midline of leg. The dominant perforators were observed in middle 1/3rd of the leg. The average pedicle length was 12.04 ± 3.27 cm. The intramuscular courses of pedicles were observed in deep and superficial strata in 65.7 and 34.3%, respectively. The MSA originated from popliteal artery in 62.5% and common sural artery in 37.5%. An accessory MSA was found in 12.5%. Type I and Type III branching patterns of MSA were observed in 31.2% each whereas Type II was found in 37.5%. The mean external diameter of perforators and MSA were 0.85 ± 0.27 mm and 2.2 ± 0.43 mm, respectively. The metrical presentation of this study provides an easy access to know about the distribution of perforators and branching pattern of MSA which will help the surgeons to make a convenient plan to harvest the MSA perforator flap in Nepalese population.

Carry Select Adder (CSA) is faster than any other adders used in many data-processing processors to achieve arithmetic functions speedily. By observing the structure of the CSA, it is clear that there is way for reducing the delay and power consumption. This work uses a sophisticated and efficient gate-level modification to significantly reduce the delay and power of the carry select adder. From the structure of the CSA, it is clear that there is scope for reducing the delay and power consumption in the CSA. Based on this modification 8-, 16-, 32-, and 64-bit CSA architecture have been developed and compared with the regular CSA architecture. The proposed design has reduced delay as compared with the regular CSA with only a slight increase in area. This work evaluates the performance of the proposed designs in terms of delay, area, power, and their products by hand with using tools like synopsis (VCS), Xilinx ISE. The results analysis shows that the proposed CSA structure is better than the regular CSA according to delay.

In cognitive radio network spectrum sensing is a fundamental element to detect the presence of primary user. In this paper cooperative spectrum sensing scheme is employed to optimize the performance in fading channel environments. This paper will focus on AWGN, rayleigh and rician fading channels to compare the error rates for ‘n’ number of cognitive users. Neyman's hypothesis testing and data fusion rules are used to satisfy and compare the error bounds in a large network for different values of SNR. Finally error rates are compared by considering different threshold values and keeping the SNR as fixed. This comparative analysis can be extended further for double threshold energy detection scheme.