This study aims to examine the new labels for the significant characterization of sleep disorder, in the context of sleep microstructure, as revealed by cyclic alternating pattern (CAP). The observation of sleep disorders through ceaseless recording and monitoring of electrocardiogram (ECG) signal assist to take protective care upon cardiovascular diseases (CVDs). Differences between normal subject and sleep disordered subject are scrutinized by estimating the heart rate. The variation of CAP events is then studied in distinct scales by the use of wavelet analysis. The essence of CAP events is considered in terms of electrocardiography (ECG) complexity analysis for various type of events. Few assumptions regarding the microstructure difference in sleep disorders including quality sleep, asymmetric activation-deactivation patterns, high duration of desynchronization phases, produces complexity in CVD analysis. This study expands the existing holdings between the increased CAP rate and the instable characteristics of sleep, highlight the differences in sleep disorders with categorization analysis. This study creates a new aspect for the interpretation of the CAP events through energy-based spectrum. The detection of sleep disorders including insomnia, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), rapid eye movement sleep behavior disorder (RBD), and sleep disordered breathing (SDB) with normal is performed. The proposed methodology employed adaptive superresolution transform (ASLT) with Inception-v3 deep neural network attained an accuracy of 98.2% using CAP sleep dataset for sleep disorder detection. The proposed methodology outperforms the state-of-the-art methods namely short time Fourier transform (STFT), and continuous wavelet transform (CWT) in heart rate estimation.

In recent years, memristor emulators have been utilized in a wide range of applications including neuromorphic computing, security, analog signal processing, and other related fields. This article introduces a novel electronically tunable dual flux controlled floating memristor emulator employing single Current Controlled Differential Difference Current Conveyor (CCDDCC), a MOS capacitor, and one PMOS transistor. The core of the suggested emulator features an electronically tunable CCDDCC-based feedback circuit. The proposed circuit avoids passive components, diodes, and analog multiplier circuits. The suggested memristor has the ability to tune the area of the pinch hysteresis loop without altering the input frequency, input voltage, MOS capacitance, and active component. The circuit operates effectively within a 300 MHz frequency range and demonstrates a well-defined pinched hysteresis loop. Its performance is evaluated using 180 nm TSMC parameters, with a compact layout area of 758.12 μm². Additionally, the robustness of the circuit is thoroughly assessed through analyses of temperature variations, supply voltage fluctuations, transistor size variations, and process corner simulations.

MOTIVATION: Eukaryotic linear motifs (ELMs), or Short Linear Motifs, are protein interaction modules that play an essential role in cellular processes and signaling networks and are often involved in diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become a crucial resource for investigating motif biology and recognizing candidate ELMs in novel amino acid sequences. Users can search amino acid sequences or UniProt Accessions on the ELM resource web interface. However, as with many web services, there are limitations in the swift processing of large-scale queries through the ELM web interface or API calls, and, therefore, integration into protein function analysis pipelines is limited. RESULTS: To allow swift, large-scale motif analyses on protein sequences using ELMs curated in the ELM database, we have extended the gget suite of Python and command line tools with a new module, gget elm, which does not rely on the ELM server for efficiently finding candidate ELMs in user-submitted amino acid sequences and UniProt Accessions. gget elm increases accessibility to the information stored in the ELM database and allows scalable searches for motif-mediated interaction sites in the amino acid sequences. AVAILABILITY AND IMPLEMENTATION: The manual and source code are available at https://github.com/pachterlab/gget.

Cardiovascular disease (CVD) is one of the leading causes of the mortality rate increase. To effectively analyze wearable sensor data for providing accurate and reliable estimation of vital signs, such as heart rate (HR), the use of artificial intelligence (AI) in wearable devices is increasing. The use of AI in designing healthcare wearable sensors is crucial to the uprising of the Internet of Medical Things (IoMT). The appearance of IoMT sensor technologies lets the healthcare industry shift from vis-a-vis consulting to telemedicine. IoMT sensors have transformed the healthcare industry by improving patient safety and reducing healthcare costs. This article proposes a photoplethysmogram (PPG) enabled wearable device in an edge-IoMT computing environment that enables users to monitor their real-time health status. A deep learning approach for automatic feature extraction is proposed in this work. The deep learning algorithm learns features from a super-resolution spectrogram computed using superlet transform. In the proposed system, a PPG signal is input, and the output layer provides information on HR. The proposed framework uses two publicly available PPG data sets to train and test the proposed edge-assisted model. The model is further evaluated using an in-house acquired PPG signal data set. The proposed framework obtained a mean absolute error of 0.76, 1.01, 1.46, and 1.79 BPM for IEEE Signal Processing Cup 2015 (IEEE SPC) training, IEEE SPC test, BAMI-I, and BAMI-II data sets, respectively. The proposed edge-based IoMT framework satisfactorily predicts HR in real time using reference-signal-less PPG sensor signal.

The mathematical formulation of the two‐dimensional (2D) ‐method for designing of the 2D‐finite impulse response (FIR) filter is introduced in this study. It features the 2D‐FIR filter with narrow transition width and flatter passband and stopband response. The 2D complexity is reduced using the quadrant symmetricity concept for the reduction of filter coefficients to be evaluated. Here, the unique features of the 2D ‐method are exploited for the efficient design of the 2D‐FIR filter. To study the effectiveness of the 2D‐FIR filter using the proposed method, its performance is compared with other existing 2D‐FIR filter methods. Simulation results for five design example of 2D lowpass, highpass, bandpass, bandstop filters and 2D differentiator are presented to prove the efficacy of the proposed design in terms of passband ripple, stopband ripple, passband error, stopband error and magnitude response.