Wavelets and short time fourier transforms on ultrasonic doppler signals for pregnancy determination in sheep

Published ArticleThe reproductive status of animals is of utmost importance to the modern farmer. Decisions concerning the management of the flock are influenced by the knowledge of the percentage of animals that are pregnant at any specific time. The aim of the project was to gain knowledge for the development of an instrument that is affordable and with which a farmer can do pregnancy determination himself/herself, thereby enabling him/her to make the correct management decisions. Experimental data were obtained from pregnant Dorper ewes with the aid of a portable Doppler instrument. Using real data as input, simulations of Wavelet and Short Time Fourier Transforms (STF) were done in MathCAD. In the simulations known levels of noise were added to the Doppler signals. Satisfactory results were obtained from the simulations of Wavelet Transforms. In the simulation of the Wavelet Transforms, signals with a SNR of -6.5 dB were successfully identified. It can thus be concluded that Wavelet Transforms can be used successfully for the detection of the fetal heartbeat in noisy ultrasonic Doppler signals

Tehohoitopotilaiden neuromonitorointi

In critical illness the risk of neurological insults is high, whether because of the illness itself, or as a treatment complication. As a result, the length of hospital stay and the risk of both further morbidity and mortality are all roughly doubled. One of the major challenges is the inability to monitor a sedated, mechanically ventilated patient’s neurological symptoms during intensive care treatment, due to a lack of reliable methods. The aims of this thesis research were to identify and test potential non-invasive methods, which would be predictive of neurological outcome, showing potential as neuromonitoring methods of critical care patients unable to self-report. As a guiding theme, all tested methods could be applied to actual critical care with relative ease. Patients were included from two groups with a notably high incidence of neurological complications, namely acute liver failure patients with hepatic encephalopathy (I), and aortic surgery patients operated during hypothermic circulatory arrest (II). The first group included 20 patients, and the latter 30 patients. Late mortality and quality of life was assessed for the aortic surgery patients (III), and the postoperative development of certain blood biomarkers (IV). The tested non-invasive neuromonitoring methods included electroencephalogram (EEG) variables from frontal or fronto-temporal abbreviated monitoring, frontal near-infrared spectroscopy, transcranial Doppler ultrasound measurements of the intracranial blood flow, and finally biomarkers. The last included established biomarkers with an association with neurological complications, namely neuron-specific enolase, and protein S100β, and several interesting biomarkers normally associated with tumours and pancreatitis. Of the tested methods, the frontal EEG variables showed greatest promise, but the addition of the temporal channels did not increase sensitivity. Spectral EEG variables were predictive of the stage of hepatic encephalopathy (I), while a novel EEG variable called wavelet subband entropy was predictive of neurological outcome (I). The hemispheric asymmetry of frontal EEG was reasonably predictive of neurological outcome after aortic surgery (II). None of the other tested methods were predictive of outcome (I, II, IV), except protein S100β, which was significantly higher in the poor outcome group 48 to 72 hours after hypothermic circulatory arrest (II). The quality of life of aortic surgery patients was good after 5 to 8 years, and comparable with the general population of chronically ill patients (III). The aim of this explorative research was to identify and test non-invasive neuromonitoring methods, suitable for use in critical care. Based on the results, frontal EEG variables are promising and predict the grade of hepatic encephalopathy and neurological outcome. The other tested methods were not predictive of neurological outcome. The long-term quality of life of aortic surgery patients is very good, despite the high risk for neurological complications.Kriittisissä sairauksissa neurologisen komplikaation riski on suuri, sekä itse kriittisen sairauden että varsinaisen hoidon seurauksena. Haittatapahtuman johdosta sairaalahoidon kesto sekä sairastuvuuden ja kuolleisuuden riskit kaksinkertaistuvat. Yksi suurimmista haasteista on luotettavien menetelmien puute, joilla voitaisiin arvioida mekaanisen hengitystuen varassa olevan ja rauhoittavia lääkkeitä saavan potilaan neurologisia oireita tehohoidon aikana. Tämän väitöskirjatyön tarkoituksena oli tunnistaa ja testata lupaavia ei-kajoavia menetelmiä, jotka ennustaisivat neurologista lopputulosta, ja jotka soveltuisivat kriittisesti sairaan tehohoitopotilaan neuromonitorointiin. Kantavana teemana kaikki testatut menetelmät voitaisiin soveltaa kliiniseen työhön suhteellisen helposti. Potilaita kerättiin kahteen ryhmään, joissa neurologisten komplikaatioiden esiintyvyys on huomattavan suuri. Ensimmäinen ryhmä käsitti akuuttia maksan vajaatoimintaa ja hepaattista enkefalopatiaa sairastavat potilaat (I), toinen hypotermisen verenkierron pysäytyksen aikana rinta-aortan leikkauksen läpikäyvät potilaat (II). Ensimmäiseen ryhmään kuului 20 potilasta, jälkimmäiseen 30 potilasta. Aorttaleikatuilta potilailta arvioitiin myös elämänlaatua sekä myöhäiskuolleisuutta (III), lisäksi tiettyjen biomerkkiaineiden aorttaleikkauksen jälkeistä kehitystä ja soveltuvuutta neuromonitorointiin arvioitiin yhdessä osatyössä (IV). Tutkimuksessa arvioituihin ei-kajoaviin neuromonitorointimenetelmiin lukeutuivat otsa- ja ohimolohkon elektroenkefalografia (EEG), lähi-infrapunaspektroskopia, transkraniaalinen Doppler-ultraäänimittaus sekä verestä mitattavat biomerkkiaineet. Biomerkkiaineet kattoivat sekä vakiintuneita aivovauriota heijastavia merkkiaineita (hermostoperäinen enolaasi, proteiini S100β) että useita mielenkiintoisia merkkiaineita, jotka liittyvät kasvaintauteihin ja haimatulehdukseen. Testatuista menetelmistä otsalohkon EEG muuttujat olivat lupaavia, mutta ohimolohkon EEG lisääminen ei parantanut menetelmien herkkyyttä. EEG spektrimuuttujat ennustivat hepaattisen enkefalopatian astetta (I) luotettavasti, kun taas kokeellinen EEG-muuttuja (aalloke-alitaajuuden entropia) ennusti luotettavasti neurologista lopputulosta akuutin maksan vajaatoimintaa sairastavilla potilailla (I). Otsalohkon aivopuoliskojen EEG-rekisteröinnin hetkellinen epäsymmetria ennusti kohtalaisella tarkkuudella neurologisten päätetapahtumien esiintymisen aorttaleikatuilla potilailla (II). Muut testatut menetelmät eivät ennustaneet neurologista lopputulemaa (I, II, IV), paitsi proteiini S100β, joka oli merkittävästi korkeampi 48–72 tuntia leikkauksen jälkeen niillä potilailla, joiden neurologinen toipuminen oli huono (IV). Aorttaleikattujen potilaiden elämänlaatu oli hyvä 5–8 vuotta leikkauksen jälkeen ja verrattavissa kroonisesti sairaan väestön elämänlaatuun (III). Tämän kartoittavan tutkimuksen tarkoituksena oli tunnistaa ja testata ei-kajoavia neuromonitorointimenetelmiä, jotka soveltuvat tehohoitoon. Tulosten perusteella otsalohkon EEG-muuttujat ennustavat hepaattisen enkefalopatian astetta sekä potilaan neurologista toipumista. Muut testatut menetelmät eivät ennustaneet neurologista toipumista luotettavasti. Aorttaleikattujen potilaiden pitkäaikainen (5–8 vuoden) terveyteen liittyvä elämänlaatu on erittäin hyvä, vaikka leikkaukseen liittyy korkea aivovaurion riski

Noninvasive ambient pressure estimation using ultrasound contrast agents – invoking subharmonics for cardiac and hepatic applications

Ultrasound contrast agents (UCAs) are encapsulated microbubbles that provide a source for acoustic impedance mismatch with the blood, due to difference in compressibility between the gas contained within these microbubbles and the blood. When insonified by an ultrasound beam, these UCAs act as nonlinear scatterers and enhance the echoes of the incident pulse, resulting in scattering of the incident ultrasound beam and emission of fundamental (f0), subharmonic (f0/2), harmonic (n*f0; n ) and ultraharmonic (((2n-1)/2)*f0; n & n > 1) components in the echo response.A promising approach to monitor in vivo pressures revolves around the fact that the ultrasound transmit and receive parameters can be selected to induce an ambient pressure amplitude dependent subharmonic signal. This subharmonic signal may be used to estimate ambient pressure amplitude; such technique of estimating ambient pressure amplitude is referred to as subharmonic aided pressure estimation or SHAPE. This project develops and evaluates the feasibility of SHAPE to noninvasively monitor cardiac and hepatic pressures (using commercially available ultrasound scanners and UCAs) because invasive catheter based pressure measurements are used currently for these applications.Invasive catheter based pressure measurements pose risk of introducing infection while the catheter is guided towards the region of interest in the body through a percutaneous incision, pose risk of death due to structural or mechanical failure of the catheter (which has also triggered product recalls by the USA Food and Drug Administration) and may potentially modulate the pressures that are being measured. Also, catheterization procedures require fluoroscopic guidance to advance the catheter to the site of pressure measurements and such catheterization procedures are not performed in all clinical centers. Thus, a noninvasive technique to obtain ambient pressure values without the catheterization process is clinically helpful.While an intravenous injection is required to inject the UCAs into the body, this procedure is considered noninvasive as per the definition provided by the Center for Medicare and Medicaid Services; invasive procedures include surgical procedures as well as catheterization procedures while minor procedures such as drawing blood (which requires a similar approach as injecting UCAs) are considered noninvasive.In vitro results showed that the standard error between catheter pressures and SHAPE results is below 10 mmHg with a correlation coefficient value of above 0.9 – this experimental error of 10 mmHg is less than the errors associated with other techniques utilizing UCAs for ambient pressure estimation. In vivo results proved the feasibility of SHAPE to noninvasively estimate clinically relevant left and right ventricular (LV and RV) pressures. The maximum error in estimating the LV and RV systolic and diastolic pressures was 3.5 mmHg. Thus, the SHAPE technique may be useful for systolic and diastolic pressure estimation given that the standard recommendations require the errors for these pressure measurements to be within 5 mmHg. The ability of SHAPE to identify induced portal hypertension (PH) was also proved. The changes in the SHAPE data correlated significantly (p < 0.05) with the changes in the portal vein (PV) pressures and the absolute amplitudes of the subharmonic signal also correlated with absolute PV pressures.The SHAPE technique provides the ability to noninvasively obtain in vivo pressures. This technique is applicable not only for critically ill patients, but also for screening symptomatic patients and potentially for other clinical pressure monitoring applications, as well.Ph.D., Biomedical Engineering -- Drexel University, 201

Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces

Acoustic sensing as a novel approach for cardiovascular monitoring at the wrist

Cardiovascular diseases are the number one cause of deaths globally. An increased cardiovascular risk can be detected by a regular monitoring of the vital signs including the heart rate, the heart rate variability (HRV) and the blood pressure. For a user to undergo continuous vital sign monitoring, wearable systems prove to be very useful as the device can be integrated into the user's lifestyle without affecting the daily activities. However, the main challenge associated with the monitoring of these cardiovascular parameters is the requirement of different sensing mechanisms at different measurement sites. There is not a single wearable device that can provide sufficient physiological information to track the vital signs from a single site on the body. This thesis proposes a novel concept of using acoustic sensing over the radial artery to extract cardiac parameters for vital sign monitoring. A wearable system consisting of a microphone is designed to allow the detection of the heart sounds together with the pulse wave, an attribute not possible with existing wrist-based sensing methods. Methods: The acoustic signals recorded from the radial artery are a continuous reflection of the instantaneous cardiac activity. These signals are studied and characterised using different algorithms to extract cardiovascular parameters. The validity of the proposed principle is firstly demonstrated using a novel algorithm to extract the heart rate from these signals. The algorithm utilises the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. The HRV in the short-term acoustic recordings is found by extracting the S1 events using the relative information between the short- and long-term energies of the signal. The S1 events are localised using three different characteristic points and the best representation is found by comparing the instantaneous heart rate profiles. The possibility of measuring the blood pressure using the wearable device is shown by recording the acoustic signal under the influence of external pressure applied on the arterial branch. The temporal and spectral characteristics of the acoustic signal are utilised to extract the feature signals and obtain a relationship with the systolic blood pressure (SBP) and diastolic blood pressure (DBP) respectively. Results: This thesis proposes three different algorithms to find the heart rate, the HRV and the SBP/ DBP readings from the acoustic signals recorded at the wrist. The results obtained by each algorithm are as follows: 1. The heart rate algorithm is validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78%, mean absolute error of 0.28 bpm, limits of agreement between -1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. A high statistical agreement between the heart rate obtained from the acoustic signal and the photoplethysmography (PPG) signal is observed. 2. The HRV algorithm is validated on the short-term acoustic signals of 5-minutes duration recorded from each of the 12 subjects. A comparison is established with the simultaneously recorded electrocardiography (ECG) and PPG signals respectively. The instantaneous heart rate for all the subjects combined together achieves an accuracy of 98.50% and 98.96% with respect to the ECG and PPG signals respectively. The results for the time-domain and frequency-domain HRV parameters also demonstrate high statistical agreement with the ECG and PPG signals respectively. 3. The algorithm proposed for the SBP/ DBP determination is validated on 104 acoustic signals recorded from 40 adult subjects. The experimental outputs when compared with the reference arm- and wrist-based monitors produce a mean error of less than 2 mmHg and a standard deviation of error around 6 mmHg. Based on these results, this thesis shows the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for the continuous monitoring of heart rate and HRV, and spot measurement of the blood pressure at the wrist.Open Acces

Signal processing and machine learning techniques for Doppler ultrasound haemodynamic measurements

Haemodynamic monitoring is an invaluable tool for evaluating, diagnosing and treating the cardiovascular system, and is an integral component of intensive care units, obstetrics wards and other medical units. Doppler ultrasound provides a non-invasive, cost-effective and fast means of haemodynamic monitoring, which traditionally necessitates highly invasive methods such as Pulmonary artery catheter or transoesophageal echocardiography. However, Doppler ultrasound scan acquisition requires a highly experienced operator and can be very challenging. Machine learning solutions that quantify and guide the scanning process in an automatic and intelligent manner could overcome these limitations and lead to routine monitoring. Development of such methods is the primary goal of the presented work. In response to this goal, this thesis proposes a suite of signal processing and machine learning techniques. Among these is a new and real-time method of maximum frequency envelope estimation. This method, which is based on image-processing techniques and is highly adaptive to varying signal quality, was developed to facilitate automatic and consistent extraction of features from Doppler ultrasound measurements. Through a thorough evaluation, this method was demonstrated to be accurate and more stable than alternative state-of-art methods. Two novel real-time methods of beat segmentation, which operate using the maximum frequency envelope, were developed to enable systematic feature extraction from individual cardiac cycles. These methods do not require any additional hardware, such as an electrocardiogram machine, and are fully automatic, real-time and highly resilient to noise. These qualities are not available in existing methods. Extensive evaluation demonstrated the methods to be highly successful. A host of machine learning solutions were analysed, designed and evaluated. This led to a set of novel features being proposed for Doppler ultrasound analysis. In addition, a state of- the-art image recognition classification method, hitherto undocumented for Doppler ultrasound analysis, was shown to be superior to more traditional modelling approaches. These contributions facilitated the design of two innovative types of feedback. To reflect beneficial probe movements, which are otherwise difficult to distinguish, a regression model to quantitatively score ultrasound measurements was proposed. This feedback was shown to be highly correlated with an ideal response. The second type of feedback explicitly predicted beneficial probe movements. This was achieved using classification models with up to five categories, giving a more challenging scenario than those addressed in prior disease classification work. Evaluation of these, for the first time, demonstrated that Doppler scan information can be used to automatically indicate probe position. Overall, the presented work includes significant contributions for Doppler ultrasound analysis, it proposes valuable new machine learning techniques, and with continued work, could lead to solutions that unlock the full potential of Doppler ultrasound haemodynamic monitoring