Schr\"odinger Spectrum based Continuous Cuff-less Blood Pressure Estimation using Clinically Relevant Features from PPG Signal and its Second Derivative

The presented study aims to estimate blood pressure (BP) using photoplethysmogram (PPG) signals while employing multiple machine learning models. The study proposes a novel algorithm for signal reconstruction, which utilizes the semi-classical signal analysis (SCSA) technique. The proposed algorithm optimises the semi-classical constant and eliminates the trade-off between complexity and accuracy in reconstruction. The reconstructed signals' spectral features are extracted and incorporated with clinically relevant PPG and its second derivative's (SDPPG) morphological features. The developed method was assessed using a publicly available virtual in-silico dataset with more than 4000 subjects, and the Multi-Parameter Intelligent Monitoring in Intensive Care Units dataset. Results showed that the method attained a mean absolute error of 5.37 and 2.96 mmHg for systolic and diastolic BP, respectively, using the CatBoost supervisory algorithm. This approach met the standards set by the Advancement of Medical Instrumentation, and achieved Grade A for all BP categories in the British Hypertension Society protocol. The proposed framework performs well even when applied to a combined database of the MIMIC-III and the Queensland dataset. This study also evaluates the proposed method's performance in a non-clinical setting with noisy and deformed PPG signals, to validate the efficacy of the SCSA method. The noise stress tests showed that the algorithm maintained its key feature detection, signal reconstruction capability, and estimation accuracy up to a 10 dB SNR ratio. It is believed that the proposed cuff-less BP estimation technique has the potential to perform well on resource-constrained settings due to its straightforward implementation approach.Comment: 16 pages, 8 figures, 8 tables, submitted to Biomedical Signal Processing and Control, Elsevie

Design of Ultrafast All-Optical Pseudo Binary Random Sequence Generator, 4-bit Multiplier and Divider using 2 x 2 Silicon Micro-ring Resonators

All-optical devices are essential for next generation ultrafast, ultralow-power and ultrahigh bandwidth information processing systems. Silicon microring resonators (SiMRR) provide a versatile platform for all-optical switching and CMOS-compatible computing, with added advantages of high Q-factor, tunability, compactness, cascadability and scalability. A detailed theoretical analysis of ultrafast all-optical switching 2 x 2 SiMRRs has been carried out incorporating the effects of two photon absorption induced free-carrier injection and thermo optic effect. The results have been used to design simple and compact all-optical 3-bit and 4-bit pseudo-random binary sequence generators and the first reported designs of all-optical 4 x 4-bit multiplier and divider. The designs have been optimized for low-power, ultrafast operation with high modulation depth, enabling logic operations at 45 Gbps.Comment: 13 pages, 4 figures. Submitted at Journal (Optik) for publicatio

Signal Quality Assessment of Photoplethysmogram Signals using Quantum Pattern Recognition and lightweight CNN Architecture

Photoplethysmography (PPG) signal comprises physiological information related to cardiorespiratory health. However, while recording, these PPG signals are easily corrupted by motion artifacts and body movements, leading to noise enriched, poor quality signals. Therefore ensuring high-quality signals is necessary to extract cardiorespiratory information accurately. Although there exists several rule-based and Machine-Learning (ML) - based approaches for PPG signal quality estimation, those algorithms' efficacy is questionable. Thus, this work proposes a lightweight CNN architecture for signal quality assessment employing a novel Quantum pattern recognition (QPR) technique. The proposed algorithm is validated on manually annotated data obtained from the University of Queensland database. A total of 28366, 5s signal segments are preprocessed and transformed into image files of 20 x 500 pixels. The image files are treated as an input to the 2D CNN architecture. The developed model classifies the PPG signal as `good' or `bad' with an accuracy of 98.3% with 99.3% sensitivity, 94.5% specificity and 98.9% F1-score. Finally, the performance of the proposed framework is validated against the noisy `Welltory app' collected PPG database. Even in a noisy environment, the proposed architecture proved its competence. Experimental analysis concludes that a slim architecture along with a novel Spatio-temporal pattern recognition technique improve the system's performance. Hence, the proposed approach can be useful to classify good and bad PPG signals for a resource-constrained wearable implementation.Comment: 7 pages, 6 figures, submitted to IEEE EMBC 202

Boosting Algorithms based Cuff-less Blood Pressure Estimation from Clinically Relevant ECG and PPG Morphological Features

Blood Pressure (BP) is often coined as a critical physiological marker for cardiovascular health. Multiple studies have explored either Photoplethysmogram (PPG) or ECG-PPG derived features for continuous BP estimation using machine learning (ML); deep learning (DL) techniques. Majority of those derived features often lack a stringent biological explanation and are not significantly correlated with BP. In this paper, we identified several clinically relevant (bio-inspired) ECG and PPG features; and exploited them to estimate Systolic (SBP), and Diastolic Blood Pressure (DBP) values using CatBoost, and AdaBoost algorithms. The estimation performance was then compared against popular ML algorithms. SBP and DBP achieved a Pearson’s correlation coefficient of 0.90 and 0.83 between estimated and target BP values. The estimated mean absolute error (MAE) values are 3.81 and 2.22 mmHg with a Standard Deviation of 6.24 and 3.51 mmHg, respectively, for SBP and DBP using CatBoost. The results surpassed the Advancement of Medical Instrumentation (AAMI) standards. For the British Hypertension Society (BHS) protocol, the results achieved for all the BP categories resided in Grade A. Further investigation reveals that bio-inspired features along with tuned ML models can produce comparable results w.r.t parameter-intensive DL networks. ln(HR × mNPV), HR, BMI index, ageing index, and PPG-K point were identified as the top five key features for estimating BP. The group-based analysis further concludes that a trade-off lies between the number of features and MAE. Increasing the no. of features beyond a certain threshold saturates the reduction in MAE

Low-complexity Reinforcement Learning Decoders for Autonomous, Scalable, Neuromorphic intra-cortical Brain Machine Interfaces

General Description. This dataset consists of data from four BMI experiments performed on two adult macaques. Three of the experiments were joystick controlled tasks, and one of them was center-out reaching task. The macaques were able to use a wireless integrated system to control a robotic platform, over which they were sitting, to achieve independent mobility using the neuronal activity in their motor cortices. The activity of populations of single neurons was recorded using multiple electrode arrays implanted in the arm region of primary motor cortex. A general description is provided below: A titanium head post (Crist Instruments, MD, USA) was affixed prior to implantation of microelectrode arrays in both NHPs. In NHP-A, 4 microelectrode arrays containing 16 electrodes each, and in NHP-B, 1 microelectrode array containing 100 electrodes were implanted in the hand/arm region of the left primary motor cortex respectively. Spike signals were acquired using an in-house 100-channel wireless neural recording system, which is sampled at 13 KHz. The wide-band signals were then band-pass filtered between 300 to 3000 Hz to remove low-frequency components. The threshold for spike deterction was found using the formula: (Thr = 5σ; σ = median(|x|/0.6745), where x is the filtered signal, and σ is an estimate of the standard deviation of the background noise. The behavorial task was to make a robotic wheelchair bound control its motion through a three-directional spring-loaded joystick (Experiment 1, 2, and 3). The experiment comprised of four tasks - a) turning 90° right, b) moving forward by 2m, c) turning 90° left, and d) staying still for 5 seconds (stop task). Successful task completion varied from experiment to experiment. Experiment 4 also involved joystick control but the primate was trained to perform classical center-out task. Data for Experiment 1 and 3 are already publicly available at: https://osf.io/dce96/. However, a detailed description is also provided here. The data are grouped in form of folders containing data for NHP-1/2-Set 1/2. For the folder, NHP 1 Set 1, experiment 1 data consists of sessions 1,2,3; expt 3: 5,6,7,8. Similarly for Set 2: expt-1: 3,4,5,10,11; expt 3: 8,9. For the folder, NHP 2 Set 1, expt 1 consists of sessions 10,11,12,13,18,19,20,21; expt 3: 15,16,17,24. For the folder. Similarly for Set 2: expt-1: 1,2,3,10,11,12,13; expt 3: 6,7,8,9. Possible use cases. These data are ideal for designing, training, and testing iBMI decoders. We expect that the dataset will be valuable for researchers who wish to design improved models of sensorimotor cortical spiking or provide an equal footing for comparing different iBMI decoders. We also hope to inspire more work along neuromorphic lines and use of online Reinforcement Learning algorithms for iBMI decoders. Variable names. Each file from Experiment 1 and 3 contains data in the following format. 1. joystick_adfreq: The frequency of operation of the joystick. 2. X_Voltage: The voltage reading corresponding to the x-coordinate (while joystick operation). 3. Y_Voltage: The voltage reading corresponding to the y-coordinate (while joystick operation). 4. Spike_data(Channel Number): The Channel Number corresponding to which the neuronal data is recorded. 5. Spike_data(Cluster): Descripting the cluster on which the channels are placed. 6. Spike_data(Spike Times): The timestamp corresponding to the detection of a spike. 7. Spike_data(Spike Number): The total number of spikes calculated for a particular trial from a particular channel. 8. Spike_data(Mean Spike Waveform): The mean neuronal data (for that trial from a particular channel) describing a spike. 9. Spike_data(Spike Amplitude): The mean spike amplitude of that particular channel. 10. IMETrainingData(SentSignals): The truth labels corresponding to a particular trial. 11. IMETrainingData(Timestamps): Time stamps corresponding to each sent signal (data). 12. IMETrainingData(ReasonFail): String data; Reason if the trial failed. 13. IMETrainingData(TrialOutcomes): Trial results corresponding to successful or unsuccessful! 14. IMETrainingData(StartTime): corresponding to the beginning of each trial. 15. IMETrainingData(EndTime): corresponding to the end of each trial. For files in Experiment 2 and 4, 1. targetTest_Acc: Corresponding direction of the joystick recorded for each trial. (decoded using the decoder) 2. targetTrain: Ground truth label, corresponding to the actual direction of the joystick (for each trial) 3. testingSet_Acc: Number of spike counts from each channel (used for testing corresponding to all the sessions) 4. trainingSet: Number of spike counts from each channel (used for calibration, mostly) Contact Information. We would be delighted to hear from you if you find this dataset valuable, especially if it leads to publication. Corresponding author: A. Ghosh ; A. Basu . Citation. A. Ghosh, S. Shaikh, P. S. V. Sun, C. Libedinsky, R. So, N. Lin, H. Chen, Z. Wang, A. Basu, "Low-complexity Reinforcement Learning Decoders for Autonomous, Scalable, Neuromorphic intra-cortical Brain Machine Interfaces," IEEE Transaction on Neural Networks and Learning Systems (Under review