Статистичний алгоритм для швидкої оцінки частоти серцебиття й локалізації серцевих тонів у фонокардіограмах, записаних за допомогою електронних стетоскопів

A draft version of the algorithm for fast determination of duration of the systolic and diastolic phases of the cardiac cycle based on a statistical analysis of a digital phonocardiogram is presented. The developed technique includes localization of the cardiac tones in the record by a quantile dichotomy and modal analysis of the calculated time intervals between the adjacent detected peaks. Next, the scatter plot is constructed in terms of time intervals before and after each peak. The stability of the cardiac rhythm is determined by the density of the cluster occurring near the cross-section of two modal values plotted along the axes. At dominance of a cardiac component in the signal, the discussed approach allows quite reliable determination of the moments of the emergence of the first and second cardiac sounds tones practically on each of the cardiac cycles. This opens the possibility to analyze the variation in the duration of separate phases of heart contractions over time. Due to the work directly in the time domain and low computational costs, the algorithms of this class are suitable for application in portable diagnostic systems with limited processor power. Pages of the article in the issue: 81 - 84 Language of the article: EnglishПредставлено пілотну версію алгоритму для швидкого визначення тривалостей систолічної й діастолічної фаз серцевого циклу на базі статистичної обробки цифрової фонокардіограми. Розроблена методика включає локалізацію серцевих тонів у записі за допомогою квантильної дихотомії та модовий аналіз обчислених часових інтервалів між сусідніми знайденими піками. Після цього будується діаграма розсіювання в термінах часових інтервалів до і після кожного піку. Стабільність серцевого ритму визначається щільністю кластеру, який утворюється поблизу перетину двох модальних значень, нанесених уздовж осей. При домінуванні в сигналі кардіологічної компоненти запропонований підхід дозволяє досить надійно визначати моменти появи першого й другого серцевих тонів практично на кожному з циклів роботи серця. Це відкриває можливість для аналізу варіації тривалостей окремих фаз серцевих скорочень з плином часу. Завдяки роботі безпосередньо в часовій області й низьким обчислювальним затратам алгоритми цього класу придатні для застосування в портативних діагностичних системах з обмеженими процесорними потужностями

Fetal movements recording system using accelerometer sensor

One of the compelling challenges in modern obstetrics is the monitoring fetal wellbeing. Physicians are gradually becoming cognizant of the relationship between fetal activity, movement, welfare, and future developmental progress. Previous works have developed few accelerometer-based systems to tackle issues related to ultrasound measurement, the provision of remote s1pport and self-managed monitoring of fetal movement during pregnancy. Though, many research questions on the optimal setup in terms of body-worn accelerometers, as well as signal processing and machine learning techniques used to detect fetal movement, are still open. In this work, a new fetal movement system recorder has been proposed. The proposed system has six accelerometer sensors and ARDUINO microcontroller. The device which is interfaced with the MATLAB signal process tool has been designed to record, display and store relevant sets of fetal movements. The sensors are to be placed on the maternal abdomen to record and process physical signals originating from the fetal. Comparison of data recorded from fetal movements with ultrasound and maternal perception technique gave the following results. An accuracy of 59.78%, 85.87%,and 97.83% was achieved using the maternal perception technique, fetal movement recording system, and ultrasound respectively. The findings show that the proposed fetal movement recording system has a better accuracy rate than maternal perception technique, and can be compared with ultrasound

Signal processing methodologies for an acoustic fetal heart rate monitor

Research and development is presented of real time signal processing methodologies for the detection of fetal heart tones within a noise-contaminated signal from a passive acoustic sensor. A linear predictor algorithm is utilized for detection of the heart tone event and additional processing derives heart rate. The linear predictor is adaptively 'trained' in a least mean square error sense on generic fetal heart tones recorded from patients. A real time monitor system is described which outputs to a strip chart recorder for plotting the time history of the fetal heart rate. The system is validated in the context of the fetal nonstress test. Comparisons are made with ultrasonic nonstress tests on a series of patients. Comparative data provides favorable indications of the feasibility of the acoustic monitor for clinical use

Data Storage Based Heart and Body Temperature Measurement Device

Body health is the most important thing for human life. The heart is one of the organs of the body that is very important for humans. The heart functions to regulate the circulation of oxygen-rich blood and to carry leftover food in the body. Heart rate illustrates how hard the heart works. The heart rate needs to be monitored to find out the patient's condition. BPM measurement and temperature is an activity of measuring heart rate and body temperature. The patient’s body temperature affects the patient's rapid heart to pump blood throughout the body.  The study aims to design a heart rate and body temperature measuring device using an infrared LED as a transmitter and a photodiode as a receiver, an LM35 as the temperature sensor, and an LCD to display the final output. This tool uses an ATMega8 microcontroller as the controlling system.  The device was completed with data storage and temperature indicators.  The result of the test on the device showed that the device performed very well in measuring the heart rate and body temperature of adult patients aged 20 until 40 years old

Development of a Personal Area Network for biomedical measurements for Internet of Things (IoT)

Internet of Things is a set of ever growing technologies and specialized devices that are increasingly influential in our everyday lives. IoT is all about connecting the physical and the digital worlds in one enabling the collection of real world data and the automation of processes. IoT turns your typical device into an smart, programmable one, more capable of interacting with humans and thus enabling users to better understand their surroundings through the data collected. This data collected by the IoT devices can then be used on all kinds of contemporary services and applications. This project aims to implement an IoT application for biomedical measurements, consisting of a WSN(Wireless Sensor Network), where three sensor nodes will collect physical world measurements. This collected information will be transmitted to a routing device, that further send the information to the internet, where the user will be able to access the data in real time through a web browser and schedule some events. In order to carry out the described scenario, a Raspberry Pi and four Zolertia Z1, three working as sensor nodes and one working as a routing node were used. The Z1 mote is powered by a low power MSP430 class microcontroller. Contiki was the operating system chosen to run the sensor nodes. In this scenario, Raspberry Pi plays the role of a router, enabling the connection of the WSN network and the internet. To send the information from the nodes, a high-speed program was developed, aiming to beat the default restrictions that Contiki OS imposes on high-speed networks. The transport protocol chosen is UDP. On the receiving end, an UDP server and a python script were developed with the intent to send the collected data to our ASP.NET web server and mySQL database. Finally connectivity tests and network speed tests of the deployed system are presented

ELECTRO-MECHANICAL DATA FUSION FOR HEART HEALTH MONITORING

Heart disease is a major public health problem and one of the leading causes of death worldwide. Therefore, cardiac monitoring is of great importance for the early detection and prevention of adverse conditions. Recently, there has been extensive research interest in long-term, continuous, and non-invasive cardiac monitoring using wearable technology. Here we introduce a wearable device for monitoring heart health. This prototype consists of three sensors to monitor electrocardiogram (ECG), phonocardiogram (PCG), and seismocardiogram (SCG) signals, integrated with a microcontroller module with Bluetooth wireless connectivity. We also created a custom printed circuit board (PCB) to integrate all the sensors into a compact design. Then, flexible housing for the electronic components was 3D printed using thermoplastic polyurethane (TPU). In addition, we developed peak detection algorithms and filtering programs to analyze the recorded cardiac signals. Our preliminary results show that the device can record all three signals in real-time. Initial results for signal interpretation come from a recurrent neural network (RNN) based machine learning algorithm, Long Short-Term Memory (LSTM), which is used to monitor and identify key features in the ECG data. The next phase of our research will include cross-examination of all three sensor signals, development of machine learning algorithms for PCG and SCG signals, and continuous improvement of the wearable device

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Wrist-based Phonocardiogram Diagnosis Leveraging Machine Learning

With the tremendous growth of technology and the fast pace of life, the need for instant information has become an everyday necessity, more so in emergency cases when every minute counts towards saving lives. mHealth has been the adopted approach for quick diagnosis using mobile devices. However, it has been challenging due to the required high quality of data, high computation load, and high-power consumption. The aim of this research is to diagnose the heart condition based on phonocardiogram (PCG) analysis using Machine Learning techniques assuming limited processing power, in order to be encapsulated later in a mobile device. The diagnosis of PCG is performed using two techniques; 1. parametric estimation with multivariate classification, particularly discriminant function. Which will be explored at length using different number of descriptive features. The feature extraction will be performed using Wavelet Transform (Filter Bank). 2. Artificial Neural Networks, and specifically Pattern Recognition. This will also use decomposed version of PCG using Wavelet Transform (Filter Bank). The results showed 97.33% successful diagnosis using the first technique using PCG with a 19 dB Signal-to-Noise-Ratio. When the signal was decomposed into four sub-bands using a Filter Bank of the second order. Each sub-band was described using two features; the signal’s mean and covariance. Additionally, different Filter Bank orders and number of features are explored and compared. Using the second technique the diagnosis resulted in a 100% successful classification with 83.3% trust level. The results are assessed, and new improvements are recommended and discussed as part of future work.Teknologian valtavan kehittymisen ja nopean elämänrytmin myötä välittömästi saatu tieto on noussut jokapäiväiseksi välttämättömyydeksi, erityisesti hätätapauksissa, joissa jokainen säästetty minuutti on tärkeää ihmishenkien pelastamiseksi. Mobiiliterveys, eli mHealth, on yleisesti valjastettu käyttöön nopeaksi diagnoosimenetelmäksi mobiililaitteiden avulla. Käyttö on kuitenkin ollut haastavaa korkean datan laatuvaatimuksen ja suurten tiedonkäsittelyvaatimuksien, nopean laskentatehon ja sekä suuren virrankulutuksen vuoksi. Tämän tutkimuksen tavoitteena oli diagnosoida sydänsairauksia fonokardiogrammianalyysin (PCG) perusteella käyttämällä koneoppimistekniikoita niin, että käytettävä laskentateho rajoitetaan vastaamaan mobiililaitteiden kapasiteettia. PCG-diagnoosi tehtiin käyttäen kahta tekniikkaa 1. Parametrinen estimointi käyttäen moniulotteista luokitusta, erityisesti signaalien erotteluanalyysin avulla. Tätä asiaa tutkittiin syvällisesti käyttäen erilaisia tilastotieteellisesti kuvailevia piirteitä. Piirteiden irrotus suoritettiin käyttäen Wavelet-muunnosta ja suodatinpankkia. 2. Keinotekoisia neuroverkkoja ja erityisesti hahmontunnistusta. Tässä menetelmässä käytetään myös PCG-signaalin hajoitusta ja Wavelet-muunnos -suodatinpankkia. Tulokset osoittivat, että PCG 19dB:n signaali-kohina-suhteella voi johtaa 97,33% onnistuneeseen diagnoosiin käytettäessä ensimmäistä tekniikkaa. Signaalin hajottaminen neljään alikaistaan suoritettiin käyttämällä toisen asteen suodatinpankkia. Jokainen alikaista kuvattiin käyttäen kahta piirrettä: signaalin keskiarvoa ja kovarianssia, näin saatiin yhteensä kahdeksan ominaisuutta kuvaamaan noin yhden minuutin näytettä PCG-signaalista. Lisäksi tutkittiin ja verrattiin eriasteisia suodattimia ja piirteitä. Toista tekniikkaa käyttäen diagnoosi johti 100% onnistuneeseen luokitteluun 83,3% luotettavuustasolla. Tuloksia käsitellään ja pohditaan, sekä tehdään niistä johtopäätöksiä. Lopuksi ehdotetaan ja suositellaan käytettyihin menetelmiin uusia parannuksia jatkotutkimuskohteiksi.fi=vertaisarvioitu|en=peerReviewed