Accuracy and Reliability of Different Approaches for the Assessment of Pelvic Floor Muscle Strength: A Systematic Review.

There had been various methods employed for the evaluation of pelvic floor muscle (PFM) strength. The aim of the study was to do a systemic review of these methods for a better understanding of these techniques and to find the best appropriate method. A systemic review of the literature was done using three databases that included: PubMed, Scopus and Web of Science using the keywords "pelvic floor anatomy" and "functional anatomy of pelvic floor muscles" from 1985 to 2022. All the studies involved were analyzed for the methodologies used by the researcher, advantages, disadvantages, and the conclusion of the study. A total of 1,876 studies were found, out of which only 64 met the criteria of inclusion. In these studies, seven methods were used for the determination of PFM strength. These methods included: clinical palpation, perineometer, electromyography, dynamometer, ultrasonography, magnetic resonance imaging, and vaginal cones. The PFM cannot be calculated accurately using any one measuring technique. There is therefore no "gold standard" approach to PFM assessment. However, combining these methods will result in the best outcomes. According to the literature review, the most often employed procedures were digital palpation, perineometry, and Ultrasonography (USG).

An Energy Efficient ECG Signal Processor Detecting Cardiovascular Diseases on Smartphone.

A novel disease diagnostic algorithm for ECG signal processing based on forward search is implemented in Application Specific Integrated Circuit (ASIC) for cardiovascular disease diagnosis on smartphone. An ASIC is fabricated using 130-nm CMOS low leakage process technology. The area of our PQRST ASIC is 1.21 mm2. The energy dissipation of PQRST ASIC is 96 pJ with a supply voltage of 0.9 V. The outputs from the ASIC are fed to an Android application that generates diagnostic report and can be sent to a cardiologist via email. The ASIC and Android application are verified for the detection of bundle branch block, hypertrophy, arrhythmia and myocardial infarction using Physionet PTB diagnostic ECG database. The failed detection rate is 0.69%, 0.69%, 0.34% and 1.72% for bundle branch block, hypertrophy, arrhythmia and myocardial infarction respectively. The AV block is detected in all the three patients in the Physionet St. Petersburg arrhythmia database. Our proposed ASIC together with our Android application is the most suitable for an energy efficient wearable cardiovascular disease detection system.

An Energy efficient application specific integrated circuit for electrocardiogram feature detection and its potential for ambulatory cardiovascular disease detection.

A novel algorithm based on forward search is developed for real-time electrocardiogram (ECG) signal processing and implemented in application specific integrated circuit (ASIC) for QRS complex related cardiovascular disease diagnosis. The authors have evaluated their algorithm using MIT-BIH database and achieve sensitivity of 99.86% and specificity of 99.93% for QRS complex peak detection. In this Letter, Physionet PTB diagnostic ECG database is used for QRS complex related disease detection. An ASIC for cardiovascular disease detection is fabricated using 130-nm CMOS high-speed process technology. The area of the ASIC is 0.5 mm(2). The power dissipation is 1.73 µW at the operating frequency of 1 kHz with a supply voltage of 0.6 V. The output from the ASIC is fed to their Android application that generates diagnostic report and can be sent to a cardiologist through email. Their ASIC result shows average failed detection rate of 0.16% for six leads data of 290 patients in PTB diagnostic ECG database. They also have implemented a low-leakage version of their ASIC. The ASIC dissipates only 45 pJ with a supply voltage of 0.9 V. Their proposed ASIC is most suitable for energy efficient telemetry cardiovascular disease detection system.

An ultra low power ECG signal processor design for cardiovascular disease detection.

This paper presents an ultra low power ASIC design based on a new cardiovascular disease diagnostic algorithm. This new algorithm based on forward search is designed for real time ECG signal processing. The algorithm is evaluated for Physionet PTB database from the point of view of cardiovascular disease diagnosis. The failed detection rate of QRS complex peak detection of our algorithm ranges from 0.07% to 0.26% for multi lead ECG signal. The ASIC is designed using 130-nm CMOS low leakage process technology. The area of ASIC is 1.21 mm(2). This ASIC consumes only 96 nW at an operating frequency of 1 kHz with a supply voltage of 0.9 V. Due to ultra low power consumption, our proposed ASIC design is most suitable for energy efficient wearable ECG monitoring devices.