88 research outputs found

    Signal Processing Contributions to Contactless Monitoring of Vital Signs Using Radars

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    Vital signs are a group of biological indicators that show the status of the body’s life-sustaining functions. They provide an objective measurement of the essential physiological functions of a living organism, and their assessment is the critical first step for any clinical evaluation. Monitoring vital sign information provides valuable insight into the patient's condition, including how they are responding to medical treatment and, more importantly, whether the patient is deteriorating. However, conventional contact-based devices are inappropriate for long-term continuous monitoring. Besides mobility restrictions and stress, they can cause discomfort, and epidermal damage, and even lead to pressure necrosis. On the other hand, the contactless monitoring of vital signs using radar devices has several advantages. Radar signals can penetrate through different materials and are not affected by skin pigmentation or external light conditions. Additionally, these devices preserve privacy, can be low-cost, and transmit no more power than a mobile phone. Despite recent advances, accurate contactless vital sign monitoring is still challenging in practical scenarios. The challenge stems from the fact that when we breathe, or when the heart beats, the tiny induced motion of the chest wall surface can be smaller than one millimeter. This means that the vital sign information can be easily lost in the background noise, or even masked by additional body movements from the monitored subject. This thesis aims to propose innovative signal processing solutions to enable the contactless monitoring of vital signs in practical scenarios. Its main contributions are threefold: a new algorithm for recovering the chest wall movements from radar signals; a novel random body movement and interference mitigation technique; and a simple, yet robust and accurate, adaptive estimation framework. These contributions were tested under different operational conditions and scenarios, spanning ideal simulation settings, real data collected while imitating common working conditions in an office environment, and a complete validation with premature babies in a critical care environment. The proposed algorithms were able to precisely recover the chest wall motion, effectively reducing the interfering effects of random body movements, and allowing clear identification of different breathing patterns. This capability is the first step toward frequency estimation and early non-invasive diagnosis of cardiorespiratory problems. In addition, most of the time, the adaptive estimation framework provided breathing and heart rate estimates within the predefined error intervals, being capable of tracking the reference values in different scenarios. Our findings shed light on the strengths and limitations of this technology and lay the foundation for future studies toward a complete contactless solution for vital signs monitoring

    Impedance pneumography in respiration monitoring

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    Respiration monitoring provides health care professionals essential information about patients’ condition and can help diagnosing pulmonary diseases. The most reliable methods for assessment are obtrusive and include masks and can require performing manoeuvres that limit the usability with uncooperative patients like children or unconscious. In contrast, in hospital wards respiration rate and effort are intermittently assessed only visually during rounds at patient rooms leading to poor frequency of recording. Hence, early signs of deterioration in condition are often missed. Bioimpedance have been studied as a continuous and unobtrusive method for respiration monitoring. The technique is based on differences in electrical properties of tissues. A small current is fed through the body and voltage across is measured. Respiration and cardiac functions affect current flow and thus change the total impedance. Frequency of the applied current and geometry of the thorax cause also variation in the signal. When using bioimpedance to assess respiratory functions the method is called impedance pneumography. Despite of being an established and widely used method, there is ongoing research to improve its performance. One major challenge is its susceptibility to movement. However, signal processing algorithms advance all the time making development of wearable applications also possible. In this study, respiration is measured with bioimpedance and compared to signal from pneumotachometer. Two different electrode configurations were used to evaluate their performance in different positions, in supine, sitting and walking stationary. The study protocol included alternation between thoracic and diaphragmatic breathing at different depths. Respiration rates were determined with peak detection, advanced counting and Fast Fourier Transform (FFT) algorithms and their performances were compared. The results show that respiration rates were most accurately measured during supine position with Mason-Likar arm electrodes. No significant differences between thoracic and diaphragmatic breathing were seen whereas shallow breathing was occasionally hard to detect. The peak detection algorithm performed best having mean absolute error (MAE) of 0.47, 1.12 and 1.23 breaths per minute (bpm) for lying, sitting and walking, respectively. However, MAE values of FFT method were not comparable to other methods in most of the cases. Comparison between electrode configurations is not straightforward, as the measurements were not made simultaneously. Also, the study involved only relatively young and healthy subjects which are not the most abundant age group needing monitoring at hospitals. When considering patient monitoring applications, future studies should involve subjects with wider range of characteristics to obtain more definitive results about the performance of the impedance pneumography.Hengitystä monitoroimalla saadaan tärkeää informaatiota potilaan terveydentilasta sekä apua keuhkosairauksien diagnosointiin. Tällä hetkellä luotettavimmat menetelmät häiritsevät luonnollista hengitystä ja saattavat vaatia erityisiä hengityskuvioita, jotka eivät onnistu yhteistyökyvyttömiltä potilailta kuten lapsilta tai vakavasti sairailta. Toisaalta sairaaloiden osastoilla hengitystaajuutta ja hengityksen vaikeutta saatetaan ajoittain arvioida ainoastaan visuaalisesti tarkastuskierrosten aikana, jolloin tuloksien väli saattaa venyä pitkäksi eikä muutoksia huomata ajoissa. Bioimpedanssimenetelmä tarjoaa keinon jatkuvaan hengityksen monitorointiin häiritsemättä sitä. Tekniikka perustuu kudosten erilaiseen kykyyn vastustaa sähkövirran kulkua, ja sen avulla voidaan saada monesta kehontoiminnosta tietoa. Hengityksen analysoinnissa menetelmästä käytetään nimitystä impedanssipneumografia. Käytännössä kehoon syötetään pieniamplitudista virtaa ja mitataan jännitettä mittapisteiden välillä. Hengityksen aiheuttamat muutokset vaikuttavat sähkövirran kulkuun ja näin ollen muuttavat impedanssisignaalia. Myös syötetyn virran taajuus sekä rintakehän muoto vaikuttavat havaittuun impedanssiin. Vaikka menetelmä on jo vakiintunut ja laajalti käytössä, tutkijat pyrkivät jatkuvasti parantamaan bioimpedanssin mittaustekniikkaa. Yksi menetelmän heikkouksista on sen alttius liikkeestä aiheutuville häiriöille. Signaalinkäsittelymenetelmät kehittyvät kuitenkin jatkuvasti mahdollistaen myös tutkimuksen bioimpedanssin käytöstä puettavissa laitteissa. Tässä tutkimuksessa mitattiin hengitystä bioimpedanssin avulla ja verrattiin saatua signaalia pneumotakometrillä kerättyyn referenssiin. Mittauksissa käytettiin kahta eri elektrodien sijoittelua ja arvioitiin niiden toimivuutta eri asennoissa: selinmakuulta, istualtaan sekä paikallaan kävellessä. Mittausten aikana tutkittavat hengittivät eri syvyyksillä ja vaihtelivat pallea- ja rintahengityksen välillä. Saadusta datasta arvioitiin hengitystaajuutta peak detection, advanced counting ja Fast Fourier Transform -algoritmeilla ja vertailtiin niiden toimivuutta. Tutkimuksessa havaittiin, että luotettavin arvio hengitystaajuudesta saatiin makuuasennossa Mason-Likar käsielektrodeilta mitattaessa. Pallea- ja rintahengityksen välillä ei havaittu merkittäviä eroja, kun taas pinnallista hengitystä oli ajoittain vaikea havaita impedanssisignaalista. Peak detection -algoritmin suoriutui parhaiten käytetyistä metodeista. Tällä menetelmällä keskimääräinen absoluuttinen virhe oli 0.47 hengitystä minuutissa (bpm) makuulta, 1.12 bpm istualtaan ja 1.23 bpm kävellessä. FFT-algoritmilla ei saatu vertailukelpoisia arvoja hengitystiheydestä johtuen todennäköisesti liian suuresta ikkunan pituudesta. Elektrodipaikkojen vaikutusta on hankala arvioida suoraviivaisesti, sillä mittauksia ei tehty samanaikaisesti. Tutkimuksessa oli mukana ainoastaan suhteellisen nuoria ja hyväkuntoisia henkilöitä. Tällaisilta tutkittavilta mahdollisesti saadaan parempia tuloksia kuin ikääntyneiltä tai ylipainoisilta. Potilasmonitorisovelluksia ajatellen seuraaviin tutkimuksiin kannattaisi ottaa mukaan ominaisuuksiltaan laajempi joukko tutkittavia, jotta saataisiin kattavampi näyttö impedanssipneumografian suorituskyvyst

    Signal Processing Using Non-invasive Physiological Sensors

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    Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

    Automatic Pain Assessment by Learning from Multiple Biopotentials

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    Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation

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    The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients’ readiness, there is still around 15–20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation –being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness
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