Blood pressure prediction from speech recordings

Ankishan, Haydar

doi:10.1016/j.bspc.2019.101842

Cited by 10 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As for hypertension, we found a higher F2, which may be related to the pressure exerted by the vessels of the vocal cords, leading to alterations in their vibration. Actually, previous studies have concluded that vowel sounds are closely related to blood pressure (Ankışhan, 2020). Thus, it has been possible to use these and other speech measures to detect high blood pressure (Saloni Sharma and Gupta, 2014).…”

Section: Discussionmentioning

confidence: 99%

Speech biomarkers of risk factors for vascular dementia in people with mild cognitive impairment

Martínez-Nicolás

Llorente

Martínez‐Sánchez

et al. 2022

Front. Hum. Neurosci.

View full text Add to dashboard Cite

IntroductionIn this study we intend to use speech analysis to analyze the cognitive impairments caused by pathologies of vascular origin such as diabetes, hypertension, hypercholesterolemia and heart disease, predictors of the development of vascular dementia.MethodsIn this study, 40 participants with mild cognitive impairment were asked to read while being recorded and they were inquired about their history of the aforementioned conditions. Their speech was then analyzed.ResultsWe found that some speech parameters of frequencies and syllabic rhythm vary due to these pathologies. In addition, we conducted a discriminant analysis in which we found that diabetes and hypertension can be predicted with an accuracy over 95% with few speech parameters, and hypercholesterolemia and heart disease with an accuracy over 80%.DiscussionThe predictor parameters found are heterogeneous, including voice quality, amplitude, frequency, and rhythm parameters. This result may lead to investigate why such important qualitative changes occur in the voice of older adults with these pathologies. Rather than trying to find a diagnostic procedure already existing in classical medicine, we expect this finding to contribute to explore the causes and concomitant pathologies of these diseases. We discuss the implications of behavioral traits, such as speech, as digital biomarkers.

show abstract

Section: Discussionmentioning

confidence: 99%

Speech biomarkers of risk factors for vascular dementia in people with mild cognitive impairment

Martínez-Nicolás

Llorente

Martínez‐Sánchez

et al. 2022

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…In other words, feature selection, called size optimization, is an algorithm that eliminates irrelevant features in the dataset [ 32 ]. Feature selection is applied to increase the performance of machine learning algorithms and reduce the size of the dataset containing the feature entries and the computational load [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

“…Although its measurement is relatively difficult compared to PPG, some studies use oscillometric waveforms [ 29 , 30 ], auscultatory and oscillometric waveforms [ 31 ], and peripheral signals [ 26 ]. In addition to these, speech-based measurement methods are also available [ 32 ]. These studies applied signal processing processes, and machine learning algorithms were used in BP estimation according to the need.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection

Kandaz

Uçar

Polat

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Objective. Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diastolic blood pressure systems with a new technological infrastructure with a noninvasive process. The study is aimed at determining the minimum ECG time required for calculating systolic and diastolic blood pressure based on the Electrocardiography (ECG) signal. Methodology. The study includes ECG recordings of five individuals taken from the IEEE database, measured during daily activity. For the study, each signal was divided into epochs of 2-4-6-8-10-12-14-16-18-20 seconds. Twenty-five features were extracted from each epoched signal. The dimension of the dataset was reduced by using Spearman’s feature selection algorithm. Analysis based on metrics was carried out by applying machine learning algorithms to the obtained dataset. Gaussian process regression exponential (GPR) machine learning algorithm was preferred because it is easy to integrate into embedded systems. Results. The MAPE estimation performance values for diastolic and systolic blood pressure values for 16-second epochs were 2.44 mmHg and 1.92 mmHg, respectively. Conclusion. According to the study results, it is evaluated that systolic and diastolic blood pressure values can be calculated with a high-performance ratio with 16-second ECG signals.

show abstract

“…Several studies have utilized signal-derived BP measurement such as photoplethysmogram and electrocardiogram signals as the indicators of BP [5][6][7]. A recent study also proposed that BP can estimate using acoustic characteristics [8]. Such novel strategies are also gradually being developed to be compatible with smartphone health applications and wearable devices.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method in Predicting Hypertension Using Facial Images

Ang

Yim

2021

Applied Sciences

View full text Add to dashboard Cite

Hypertension has been a crucial public health challenge among adults. This study aimed to develop a novel method for non-contact prediction of hypertension using facial characteristics such as facial features and facial color. The data of 1099 subjects (376 men and 723 women) analyzed in this study were obtained from the Korean Constitutional Multicenter Study of Korean medicine Data Center (KDC) at the Korea Institute of Oriental Medicine (KIOM). Facial images were collected and facial variables were extracted using image processing techniques. Analysis of covariance (ANCOVA) and Least Absolute Shrinkage and Selection Operator (LASSO) were performed to compare and identify the facial characteristic variables between the hypertension group and normal group. We found that the most distinct facial feature differences between hypertension patients and normal individuals were facial shape and nose shape for men in addition to eye shape and nose shape for women. In terms of facial colors, cheek color in men, as well as forehead and nose color in women, were the most distinct facial colors between the hypertension groups and normal individuals. Looking at the AUC value, the prediction power for women is better than men. In conclusion, we managed to explore and identify the facial characteristics variables related to hypertension. This study may provide new evidence in the validity of predicting hypertension using facial characteristics.

show abstract

Blood pressure prediction from speech recordings

Cited by 10 publications

References 27 publications

Speech biomarkers of risk factors for vascular dementia in people with mild cognitive impairment

Speech biomarkers of risk factors for vascular dementia in people with mild cognitive impairment

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection

A Novel Method in Predicting Hypertension Using Facial Images

Contact Info

Product

Resources

About