A Novel Deep Learning based Automatic Auscultatory Method to Measure Blood Pressure

Background: It is clinically important to develop innovative techniques that can accurately measure blood pressures (BP) automatically. Objectives: This study aimed to present and evaluate a novel automatic BP measurement method based on deep learning method, and to confirm the effects on measured BPs of the position and contact pressure of stethoscope. Methods: 30 healthy subjects were recruited. 9 BP measurements (from three different stethoscope contact pressures and three repeats) were performed on each subject. The convolutional neural network (CNN) was designed and trained to identify the Korotkoff sounds at a beat-by-beat level. Next, a mapping algorithm was developed to relate the identified Korotkoff beats to the corresponding cuff pressures for systolic and diastolic BP (SBP and DBP) determinations. Its performance was evaluated by investigating the effects of the position and contact pressure of stethoscope on measured BPs in comparison with reference manual auscultatory method. Results: The overall measurement errors of the proposed method were 1.4 ± 2.4 mmHg for SBP and 3.3 ± 2.9 mmHg for DBP from all the measurements. In addition, the method demonstrated that there were small SBP differences between the 2 stethoscope positions, respectively at the 3 stethoscope contact pressures, and that DBP from the stethoscope under the cuff was significantly lower than that from outside the cuff by 2.0 mmHg (P < 0.01). Conclusion: Our findings suggested that the deep learning based method was an effective technique to measure BP, and could be developed further to replace the current oscillometric based automatic blood pressure measurement method

The impact of arm position and pulse pressure on the validation of a wrist-cuff blood pressure measurement device in a high risk population

Despite the increasing popularity of blood pressure (BP) wrist monitors for self-BP measurement at home, device validation and the effect of arm position remains an issue. This study focused on the validation of the Omron HEM-609 wrist BP device, including an evaluation of the impact of arm position and pulse pressure on BP measurement validation. Fifty patients at high risk for cardiovascular disease were selected (age 65 ± 10 years). Each patient had two measurements with a mercury sphygmomanometer and three measurements with the wrist BP device (wrist at the heart level while the horizontal arm supported [HORIZONTAL], hand supported on the opposite shoulder [SHOULDER], and elbow placed on a desk [DESK]), in random order. The achieved systolic BP (SBP) and diastolic BP (DBP) wrist-cuff readings were compared to the mercury device and the frequencies of the readings within 5, 10, and 15 mmHg of the gold standard were computed and compared with the British Hypertension Society (BHS) and Association for the Advancement of Medical Instrumentation (AAMI) protocols. The results showed while SBP readings with HORIZONTAL and SHOULDER positions were significantly different from the mercury device (mean difference = 7.1 and 13.3 mmHg, respectively; P < 0.05), the DESK position created the closest reading to mercury (mean difference = 3.8, P > 0.1). Approximately 71% of SBP readings with the DESK position were within ±10 mmHg, whereas it was 62.5% and 34% for HORIZONTAL and SHOULDER positions, respectively. Wrist DBP attained category D with BHS criteria with all three arm positions. Bland–Altman plots illustrated that the wrist monitor systematically underestimated SBP and DBP values. However a reading adjustment of 5 and 10 mmHg for SBP and DBP (DESK position) resulted in improvement with 75% and 77% of the readings being within 10 mmHg (grade B), respectively. AAMI criteria were not fulfilled due to heterogeneity. The findings also showed that the mismatch between the mercury and wrist-cuff systolic BP readings was directly associated with pulse pressure. In conclusion the DESK position produces the most accurate readings when compared to the mercury device. Although wrist BP measurement may underestimate BP measured compared to a mercury device, an adjustment by 5 and 10 mmHg for SBP and DBP, respectively, creates a valid result with the DESK position. Nevertheless, considering the observed variations and the possible impact of arterial stiffness, individual clinical validation is recommended

Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

Non-invasive blood pressure estimation based on electro/phonocardiogram

Ingeniero (a) ElectrónicoPregrad

Sleep, physical activity, and health in children : a developmental perspective

Adequate rest and periods of activity are important for maintaining physiological homeostasis, for the adaptive functioning of the stress-response systems, and they promote psychological well-being. However, knowledge on the associations of sleep and physical activity with stress system functioning, and of physical activity with psychiatric problems is limited especially in children and youth. This study was designed to address three research questions, (1) whether sleep is associated with cardiovascular function in 8-year-old children, (2) whether physical activity is associated with psychiatric problems in 8-year-old children, and (3) whether physical activity is associated with hypothalamic-pituitary-adrenocortical axis (HPAA) function in 8- and 12-year-old children. The participants came from an urban community-based cohort originally comprising 1049 infants born in 1998 in Helsinki, Finland. Sleep and physical activity were objectively measured using accelerometers. Sleep was also assessed using parent-reported questionnaire-based data. Of the 413 children invited to a follow-up, 321 participated at a mean age of 8.1 years. Of these, 231 to 274 were included in the analyses of sleep and ambulatory blood pressure, or cardiovascular reactivity to the Trier Social Stress Test for Children (TSST-C). The children s mothers and teachers filled in a questionnaire reporting common childhood psychiatric problems, and 199 children had valid data on physical activity and psychiatric problems from both observers. HPAA activity was measured via salivary cortisol concentrations, 252 of the children with valid data on physical activity had data on diurnal salivary cortisol, and 248 had data on salivary cortisol responses to the TSST-C. Later, of the 920 adolescents invited to a further follow-up, 451 participated at a mean age of 12.3 years. Of these, 283 adolescents with valid physical activity data provided data on diurnal salivary cortisol, and 272 adolescents provided data on salivary cortisol responses to a low-dose overnight dexamethasone suppression test (DST), a method used to study the individual physiological variation in HPAA feedback inhibition. In contrast with a wealth of evidence especially from adults, the results showed that sleep in healthy children was not associated with an unhealthy cardiovascular phenotype. Higher physical activity levels were associated with a lower probability for psychiatric problems in children as well as lower HPAA reactivity to psychosocial stress at 8 years of age. In addition, in early adolescence (12 years of age) physical activity was associated with lower morning cortisol levels in girls and higher HPAA suppression in response to the DST in boys. These results provide evidence on the health-related associations of sleep and physical activity in a community-based cohort of children. These findings offer insight into the influence of physical activity on physical and mental well-being, by suggesting that physical activity could promote health by moderating HPAA function. As the results are correlational in nature, further research using a prospective controlled methodology is called for. This study emphasizes the importance of sustaining and supporting high physical activity levels throughout childhood and adolescence.Riittävä lepo ja fyysinen aktiivisuus ovat keskeisiä fysiologisen tasapainon ja stressijärjestelmien toiminnan kannalta. Molemmat tekijät myös tukevat psyykkistä hyvinvointia. Erityisesti lapsia ja nuoria koskeva tieteellinen tieto unen ja fyysisen aktiivisuuden yhteyksistä stressijärjestelmien toimintaan, ja fyysisen aktiivisuuden yhteyksistä psykiatriseen oireiluun, on kuitenkin vielä vähäistä. Tämä väitöskirja vastaa kolmeen tutkimuskysymykseen: (1) onko unen laatu ja määrä yhteydessä kardiovaskulaarijärjestelmän aktiivisuuteen 8-vuoden iässä, (2) onko fyysinen aktiivisuus yhteydessä psykiatristen oireiden esiintyvyyteen 8-vuoden iässä, ja (3) onko fyysinen aktiivisuus yhteydessä hypotalamus aivolisäke lisämunuaiskuori-akselin (HPA-akseli) toimintaan 8- ja 12-vuoden iässä. Tutkimuksen osallistujat ovat osa kaupunkilaisväestöön pohjautuvaa seurantatutkimusta, johon osallistui alun perin 1049 vuonna 1998 Helsingissä syntynyttä lasta. Unta ja fyysistä aktiivisuutta mitattiin objektiivisesti kiihtyvyysantureilla. Unta arvioitiin myös vanhempien täyttämän kyselylomakkeen avulla. Kutsutuista 413 lapsesta 321 osallistui jatkotutkimukseen keskimäärin 8.1-vuoden iässä. Näistä lapsista 231 274 osallistui analyyseihin unen ja vuorokausiverenpaineen yhteyksistä tai unen yhteyksistä kardiovaskulaariseen reaktiivisuuteen psykososiaalisessa stressitestissä (lapsille muokattu Trierin Sosiaalinen Stressikoe, TSST-C). Lasten äidit ja opettajat täyttivät yleisimpiä lasten psykiatrisia oireita koskevan kyselyn. Käyttökelpoista tutkimusaineistoa sekä fyysisestä aktiivisuudesta että psykiatrisista oireista molemmilta havainnoitsijoilta saatiin 199 lapselta. HPA-akselin toimintaa arvioitiin syljestä mitattujen kortisolitasojen avulla. Käyttökelpoista aineistoa sekä fyysisestä aktiivisuudesta että vuorokausikortisolista saatiin 252 lapselta ja käyttökelpoista aineistoa fyysisestä aktiivisuudesta ja kortisolitasoista TSST-C:n jälkeen saatiin 248 lapselta. Myöhemmin 920 nuorta kutsuttiin uuteen jatkotutkimukseen keskimäärin 12.3-vuoden iässä, ja heistä 451 osallistui. Käyttökelpoista aineistoa sekä fyysisestä aktiivisuudesta että vuorokausikortisolista saatiin 283 nuorelta. Lisäksi 248 nuorelta saatiin käyttökelpoista aineistoa fyysisestä aktiivisuudesta ja kortisolitasoista yön yli tehdyn matala-annoksisen deksametasonisuppressiotestin (DST) jälkeen, jonka avulla tutkittiin yksilöllistä fysiologista vaihtelua HPA-akselin negatiivisen palautejärjestelmän toiminnassa. Useista aikaisemmista tutkimustuloksista poiketen terveillä 8-vuotiailla lapsilla uni ei ollut yhteydessä sydän- ja verisuonitautien riskiä lisäävään kardiovaskulaariseen fenotyyppiin. 8-vuoden iässä fyysisesti aktiivisemmilla lapsilla oli matalampi riski kärsiä psykiatrisista oireista. Lisäksi aktiivisten lasten HPA-akselin reaktiivisuus psykososiaaliseen stressiin oli vähän liikkuvia lapsia matalampi. Varhaisessa murrosiässä (12-vuotiaana) tyttöjen korkeampi fyysinen aktiivisuus oli yhteydessä matalampiin kortisolitasoihin aamulla, kun taas aktiivisemmilla pojilla HPA-akselin suppressio DST:n jälkeen oli suurempaa. Nämä tulokset lisäävät tieteellistä näyttöä unen ja fyysisen aktiivisuuden yhteyksistä hyvinvointiin lapsilla ja nuorilla. Tulokset saattavat myös tuoda lisää ymmärrystä fyysisen aktiivisuuden ja psyykkisen hyvinvoinnin yhteyksiä selittäviin malleihin näyttämällä, että liikunta saattaa tukea hyvinvointia säätelemällä HPA-akselin toimintaa. Koska tämän tutkimuksen löydökset ovat korrelatiivisia, tarvitaan jatkossa myös kokeellisia pitkittäistutkimuksia, jotta voitaisiin perehtyä löydösten syy seuraus -suhteisiin. Tämän tutkielman löydökset korostavat erityisesti liikuntaan kannustamisen ja liikunnan määrän ylläpitämisen tärkeyttä lapsuudesta varhaiseen murrosikään

Wearable RF sensors for non-invasive detection of blood-glucose levels

PhDRadio frequency (RF) techniques have the potential to provide blood glucose readings through sensing the glucose dependent change in dielectric properties of the biological tissue. Such technique can enable much desired non-invasive and continuous monitoring of blood glucose level. In this work, we present realistic glucose dependence of dielectric properties as well as basic understanding of resonator behaviour while radiating towards the lossy biological tissue. To investigate the potential of RF techniques, two resonators, operating at microwave frequencies when placed radiating towards the biological tissue, are designed and fabricated. The spiral resonator is tested with liquid and semi-solid phantoms containing different amounts of sugar. An analytical formulation to retrieve the dielectric properties of the biological tissues is improved. In order to perform realistic tests, novel tissue mimicking materials for an extremely wide frequency range are proposed. Glucose dependance of the blood mimicking material dielectric properties are further investigated by adding realistic glucose amounts to the blood mimicking material and dielectric spectroscopy is performed. Next, a single pole Cole-Cole model is fitted to the median of the dielectric property measurements. In addition, a patch resonator is simulated with four-layered digital phantom and tested with the four-layered physical tissue mimicking phantom. Finally, a double parameter measurement platform is constructed by combining the patch resonator and a commercial force sensor to perform controlled experiments with humans. Also, the force dependant response of the patch resonator is quantified. Soda tests is performed on five subjects with the platform, all subjects were asked to apply the same level of force. Spiral resonator is also applied to examine the glucose changes of two human subjects during the soda test. The results suggests that, although the glucose-dependance of the dielectric properties is relatively small, the input impedance of a microwave resonator is still sensitive to such small alterations

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology