Heart rate monitoring using human speech spectral features

This paper attempts to establish a correlation between the human speech, emotions and human heart rate. The study highlights a possible contactless human heart rate measurement technique useful for monitoring of patient condition from realtime speech recordings. The distance between the average peak-to-peak distances in speech Mel-frequency cepstral coefficients are used as the speech features. The features when tested on 20 classifiers from the data collected from 30 subjects indicate a non-separable classification problem, however, the classification accuracies indicate the existence of strong correlation between the human speech, emotion and heart-rates

Heartbeat detection by Laser Doppler Vibrometry and Machine Learning

none6openAntognoli, Luca; Moccia, Sara; Migliorelli, Lucia; Casaccia, Sara; Scalise, Lorenzo; Frontoni, EmanueleAntognoli, Luca; Moccia, Sara; Migliorelli, Lucia; Casaccia, Sara; Scalise, Lorenzo; Frontoni, Emanuel

Heartbeat detection by laser doppler vibrometry and machine learning

Background: Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting heartbeat from the carotid LDV signal. Methods: The performances of Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN) were compared using the leave-one-subject-out cross-validation as the testing protocol in an LDV dataset collected from 28 subjects. The classification was conducted on LDV signal windows, which were labeled as beat, if containing a beat, or no-beat, otherwise. The labeling procedure was performed using electrocardiography as the gold standard. Results: For the beat class, the f1-score (f 1) values were 0.93, 0.93, 0.95, 0.96 for RF, DT, KNN and SVM, respectively. No statistical differences were found between the classifiers. When testing the SVM on the full-length (10 min long) LDV signals, to simulate a real-world application, we achieved a median macro-f 1 of 0.76. Conclusions: Using machine learning for heartbeat detection from carotid LDV signals showed encouraging results, representing a promising step in the field of contactless cardiovascular signal analysis

Speech-Based Blood Pressure Estimation with Enhanced Optimization and Incremental Clustering

Blood Pressure (BP) estimation plays a pivotal role in diagnosing various health conditions, highlighting the need for innovative approaches to overcome conventional measurement challenges. Leveraging machine learning and speech signals, this study investigates accurate BP estimation with a focus on preprocessing, feature extraction, and real-time applications. An advanced clustering-based strategy, incorporating the k-means algorithm and the proposed Fact-Finding Instructor optimization algorithm, is introduced to enhance accuracy. The combined outcome of these clustering techniques enables robust BP estimation. Moreover, extending beyond these insights, this study delves into the dynamic realm of contemporary digital content consumption. Platforms like YouTube have emerged as influential spaces, presenting an array of videos that evoke diverse emotions. From heartwarming and amusing content to intense narratives, YouTube captures a spectrum of human experiences, influencing information access and emotional engagement. Within this context, this research investigates the interplay between YouTube videos and physiological responses, particularly Blood Pressure (BP) levels. By integrating advanced BP estimation techniques with the emotional dimensions of YouTube videos, this study enriches our understanding of how modern media environments intersect with health implications.Comment: 29 pages, 2 tables, 9 figure

Methods for acquisition and integration of personal wellness parameters

Wellness indicates the state or condition of being in good physical and mental health. Stress is a common state of emotional strain that plays a crucial role in the everyday quality of life. Nowadays, there is a growing individual awareness of the importance of a proper lifestyle and a generalized trend to become an active part in monitoring, preserving, and improving personal wellness for both physical and emotional aspects. The majority studies in this field relies on the evaluation of the changes of sensed parameters passing from rest to “maximal” stress. However, the vast majority of people usually experiences stressing circumstances in everyday life. This led us to investigate the impact of mild cognitive activation which can be somehow comparable to usual situations that everyone can face in daily life. Several signals and data can be useful to characterize the state of a person, but not all of them are equally important. So it is crucial to analyse the mutual relevance of the different pieces of information. In this work we focus on a subset of well-established psychophysical descriptors and we identified a set of devices enabling the measurement of these parameters . The design of the experimental setup and the selection of sensing devices were driven by qualitative criteria such as intrusiveness, reliability, and ease of use. These are deemed crucial for implementing effective (self-)monitoring strategies. A reference dataset, named “Mild Cognitive Activation” (MCA), was collected. The last aim of the project was the definition of a quantitative model for data integration providing a concise description of the wellness status of a person. This process was based on unsupervised learning paradigms. Data from MCA were integrated with data from the “Stress Recognition in Automobile Drivers” dataset . This allowed a cross validation of the integration methodology

Development of normal ranges for cardiopulmonary exercise testing performed using arm ergometry

Introduction: The performance of a cardiopulmonary exercise test (CPET) requires an individual to undertake a progressive, maximal exercise test to a symptom limited end point. CPET is standardly performed using a treadmill or cycle ergometer (CE). There is a growing cohort of patients in whom the performance of a CE or treadmill test is not possible. Arm ergometry (AE) is an alternative exercise modality to CE, however, AE achieves lower oxygen uptake (V̇O2) values as it involves smaller muscle groups and generates less cardiovascular stress. Current predicted equations for the interpretation of AE CPET are limited by small sample sizes, gender bias and limited age ranges. Aims: To develop predicted equations and reference ranges for AE exercise testing and to compare the results of AE CPET to those obtained from CE. Methods: Maximal CPET to volitional exhaustion was performed in a group of 116 (62 F) healthy volunteers of median age 38 (IQR 19) years, using both AE and CE with randomised testing order and a rest interval of at least 24 hours. Breath by breath gas analysis was performed using the Ultima CPX (Medical Graphics, UK) metabolic cart. Regression analysis was used to develop regression equations for AE V̇O2, work rate, anaerobic threshold and heart rate. Results: The model with dependent variable AE V̇O2 ml.min-1 and independent variables age (years), sex (0 male, 1 female) and weight (kg) fit the population with a r2 = 0.542 and adjusted r2 = 0.53. The equation estimated with this model was 1930.803 - (12.651 x age) – (756.095 x sex) + (10.507 x weight). Equations for peak work rate, anaerobic threshold and heart rate were also developed. Results demonstrated that AE exercise parameters were significantly lower than those obtained from CE. Conclusions: These findings represent the largest and most diverse set of predicted values and reference ranges for AE CPET parameters in healthy individuals to date. Implementation of these reference equations will allow AE to be more widely adopted enabling the performance and interpretation of CPET in a wider population