Fiducial points of photoplethysmogram (PPG), first derivative PPG (VPG), and second derivative PPG (APG) are essential in extracting numerous parameters to diagnose cardiovascular disease. However, the fiducial points were usually detected using complex mathematical algorithms. Inflection points from derivatives waveforms are not thoroughly studied, whereas they can significantly assist in peak detection. This study is performed to investigate the derivative waveforms of PPG and use them to detect the important peaks of PPG, VPG, and APG. PPGs with different morphologies from 43 ischemic heart disease subjects are analyzed. Inflection points of the derivative waveforms up to the fourth level are observed, and consistent information (derivative markers) is used to detect the fiducial points of PPG, VPG, and APG with proper sequence. Moving average filter and simple thresholding techniques are applied to detect the primary points in VPG and the third derivative waveform. A total of twelve out of twenty derivative markers are found reliable in detecting fiducial points of two common types of PPG. Systolic peaks are accurately detected with 99.64% sensitivity and 99.38% positive predictivity using the 43 IHD dataset and Complex System Laboratory (CSL) Pulse Oximetry Artifact Labels database. The study has introduced the fourth derivative PPG waveform with four main points, which are significantly valuable for detecting the fiducial points of PPG, VPG, and APG.

Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity that is diagnosed visually by analyzing electroencephalography (EEG) signals. Background: Surgical operations are the only option for epilepsy treatment when patients are refractory to treatment, which highlights the role of classifying focal and generalized epilepsy syndrome. Therefore, developing a model to be used for diagnosing focal and generalized epilepsy automatically is important. Methods: A classification model based on longitudinal bipolar montage (LB), discrete wavelet transform (DWT), feature extraction techniques, and statistical analysis in feature selection for RNN combined with long short-term memory (LSTM) is proposed in this work for identifying epilepsy. Initially, normal and epileptic LB channels were decomposed into three levels, and 15 various features were extracted. The selected features were extracted from each segment of the signals and fed into LSTM for the classification approach. Results: The proposed algorithm achieved a 96.1% accuracy, a 96.8% sensitivity, and a 97.4% specificity in distinguishing normal subjects from subjects with epilepsy. This optimal model was used to analyze the channels of subjects with focal and generalized epilepsy for diagnosing purposes, relying on statistical parameters. Conclusions: The proposed approach is promising, as it can be used to detect epilepsy with satisfactory classification performance and diagnose focal and generalized epilepsy.

Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed.

This study was conducted to investigate the utility and efficacy of finger photoplethysmogram pulse amplitude (PPG-AC) in comparison with the standard Doppler ultrasound in assessing an endothelial function via flow-mediated dilation (FMD). High-resolution B-mode scanning of the right brachial artery (BA) of 31 healthy subjects aged 39.7 +/- 11.3 (range 22-64) years and 52 risk subjects aged 47.7 +/- 10.8 (range 30-65) years were performed before and after 4 min of upper arm occlusion. Concurrent with the ultrasound measurement (where color Doppler imaging was used to enhance arterial boundary detection), PPG signals were recorded from both index fingers for cross evaluation and comparison. Our results show that the finger PPG-AC exhibits a similar response to that of the well-known BA dilation: following the release of pressure (cuff around the BA), the PPG-AC increases abruptly before slowly decreasing toward the baseline. The peak PPG-AC is reached significantly faster than the peak FMD measured by ultrasound among healthy and risk groups (P < 0.001). The proposed technique using a finger photoplethysmogram can be applied in a rapid and non-invasive assessment of peripheral vascular functions as an alternative low-cost and less operator-dependent tool compared to ultrasound.

Flexible sensors have been extensively employed in wearable technologies for physiological monitoring given the technological advancement in recent years. Conventional sensors made of silicon or glass substrates may be limited by their rigid structures, bulkiness, and incapability for continuous monitoring of vital signs, such as blood pressure (BP). Two-dimensional (2D) nanomaterials have received considerable attention in the fabrication of flexible sensors due to their large surface-area-to-volume ratio, high electrical conductivity, cost effectiveness, flexibility, and light weight. This review discusses the transduction mechanisms, namely, piezoelectric, capacitive, piezoresistive, and triboelectric, of flexible sensors. Several 2D nanomaterials used as sensing elements for flexible BP sensors are reviewed in terms of their mechanisms, materials, and sensing performance. Previous works on wearable BP sensors are presented, including epidermal patches, electronic tattoos, and commercialized BP patches. Finally, the challenges and future outlook of this emerging technology are addressed for non-invasive and continuous BP monitoring.

Photoplethysmograph (PPG) is a simple and cost effective method to assess cardiovascular related parameters such as heart rate, arterial blood oxygen saturation and blood pressure. PPG signal consists of not only synchronized heart beats, but also the rhythms of respiration. The PPG sensor, which consists of infrared light-emitting diodes (LEDs) and a photodetector, allows a simple, reliable and low-cost means of monitoring the pulse rate. In this project, PPG signals are acquired through a customized data acquisition process using Arduino board to control the pulse circuit and to obtain the PPG signals from human subjects. Using signal processing techniques, including filters, peak detections, wavelet transform analysis and power spectral density, the heart rate (HR) and breathing rate (BR) are be obtained simultaneously. Estimations of HR and BR are conducted using MATLAB algorithm developed based on the wavelet decomposition techniques to extract the heart and respiration activities from the PPG signals. The values of HR and BR obtained using the algorithm are similar to the values obtained by manual estimation for seven sample subjects where the range of percentage errors are small about 0-9.5% for the breathing rate and 2.1-5.7% for the heart rate.