603 research outputs found
A wearable system for in-home and long-term assessment of fetal movement
International audienceObjectives: This paper presents a novel wearable system for in-home and long-term fetal movementmonitoring on a reliable and easily accessible basis.Material and methods: The system mainly consists of four accelerometers for fetal movement signalacquisition, a microcontroller for signal processing and an Android-based device interacting with the mi-crocontroller via Bluetooth Low Energy (BLE), providing the mother with information related to the fetalmovement in an intelligible way.Results: The proposed system can deliver reliable results with a specicity of 0.99 and a sensitivity of0.77 for fetal movement time series signal classication.Conclusion: The proposed wearable system will provide a good alternative to optimize the use of medicalprofessionals and hospital resources, and has potential applications in the eld of e-Health home care.Besides, the fetal movement acceleration signals acquired with volunteers (pregnant women) helps establishan initial database for future medical analysis of sensor-recorded fetal behaviors
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
Antepartum Fetal Monitoring through a Wearable System and a Mobile Application
Prenatal monitoring of Fetal Heart Rate (FHR) is crucial for the prevention of fetal pathologies and unfavorable deliveries. However, the most commonly used Cardiotocographic exam can be performed only in hospital-like structures and requires the supervision of expert personnel. For this reason, a wearable system able to continuously monitor FHR would be a noticeable step towards a personalized and remote pregnancy care. Thanks to textile electrodes, miniaturized electronics, and smart devices like smartphones and tablets, we developed a wearable integrated system for everyday fetal monitoring during the last weeks of pregnancy. Pregnant women at home can use it without the need for any external support by clinicians. The transmission of FHR to a specialized medical center allows its remote analysis, exploiting advanced algorithms running on high-performance hardware able to obtain the best classification of the fetal condition. The system has been tested on a limited set of pregnant women whose fetal electrocardiogram recordings were acquired and classified, yielding an overall score for both accuracy and sensitivity over 90%. This novel approach can open a new perspective on the continuous monitoring of fetus development by enhancing the performance of regular examinations, making treatments really personalized, and reducing hospitalization or ambulatory visits. Keywords: tele-monitoring; wearable devices; fetal heart rate; telemedicin
A phonocardiographic-based fiber-optic sensor and adaptive filtering system for noninvasive continuous fetal heart rate monitoring
This paper focuses on the design, realization, and verification of a novel phonocardiographic-based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio-SNR, Root Mean Square Error-RMSE, Sensitivity-S+, and Positive Predictive Value-PPV.Web of Science174art. no. 89
Estrazione non invasiva del segnale elettrocardiografico fetale da registrazioni con elettrodi posti sullâaddome della gestante (Non-invasive extraction of the fetal electrocardiogram from abdominal recordings by positioning electrodes on the pregnant womanâs abdomen)
openIl cuore è il primo organo che si sviluppa nel feto, particolarmente nelle primissime settimane di
gestazione. Rispetto al cuore adulto, quello fetale ha una fisiologia ed unâanatomia significativamente
differenti, a causa della differente circolazione cardiovascolare. Il benessere fetale si valuta
monitorando lâattivitĂ cardiaca mediante elettrocardiografia fetale (ECGf). LâECGf invasivo (acquisito
posizionando elettrodi allo scalpo fetale) è considerato il gold standard, ma lâinvasivitĂ che lo
caratterizza ne limita la sua applicabilitĂ . Al contrario, lâuso clinico dellâECGf non invasivo (acquisito
posizionando elettrodi sullâaddome della gestante) è limitato dalla scarsa qualitĂ del segnale risultante.
LâECGf non invasivo si estrae da registrazioni addominali, che sono corrotte da differenti tipi di rumore,
fra i quali lâinterferenza primaria è rappresentata dallâECG materno. Il Segmented-Beat Modulation
Method (SBMM) è stato da me recentemente proposto come una nuova procedura di filtraggio basata
sul calcolo del template del battito cardiaco. SBMM fornisce una stima ripulita dellâECG estratto da
registrazioni rumorose, preservando la fisiologica variabilitĂ ECG del segnale originale. Questa
caratteristica è ottenuta grazie alla segmentazione di ogni battito cardiaco per indentificare i segmenti
QRS e TUP, seguito dal processo di modulazione/demodulazione (che include strecciamento e
compressione) del segmento TUP, per aggiustarlo in modo adattativo alla morfologia e alla durata di
ogni battito originario. Dapprima applicato allâECG adulto al fine di dimostrare la sua robustezza al
rumore, lâSBMM è stato poi applicato al caso fetale. Particolarmente significativi sono i risultati relativi
alle applicazioni su ECGf non invasivo, dove lâSBMM fornisce segnali caratterizzati da un rapporto
segnale-rumore comparabile a quello caratterizzante lâECGf invasivo. Tuttavia, lâSBMM può
contribuire alla diffusione dellâECGf non invasiva nella pratica clinica.The heart is the first organ that develops in the fetus, particularly in the very early stages
of pregnancy. Compared to the adult heart, the physiology and anatomy of the fetal heart
exhibit some significant differences. These differences originate from the fact that the fetal
cardiovascular circulation is different from the adult circulation. Fetal well-being
evaluation may be accomplished by monitoring cardiac activity through fetal
electrocardiography (fECG). Invasive fECG (acquired through scalp electrodes) is the
gold standard but its invasiveness limits its clinical applicability. Instead, clinical use of
non-invasive fECG (acquired through abdominal electrodes) has so far been limited by its
poor signal quality. Non-invasive fECG is extracted from the abdominal recording and is
corrupted by different kind of noise, among which maternal ECG is the main interference.
The Segmented-Beat Modulation Method (SBMM) was recently proposed by myself as a
new template-based filtering procedure able to provide a clean ECG estimation from a
noisy recording by preserving physiological ECG variability of the original signal. The
former feature is achieved thanks to a segmentation procedure applied to each cardiac
beat in order to identify the QRS and TUP segments, followed by a
modulation/demodulation process (involving stretching and compression) of the TUP
segments to adaptively adjust each estimated cardiac beat to the original beat morphology
and duration. SBMM was first applied to adult ECG applications, in order to demonstrate
its robustness to noise, and then to fECG applications. Particularly significant are the
results relative to the non-invasive applications, where SBMM provided fECG signals
characterized by a signal-to-noise ratio comparable to that characterizing invasive fECG.
Thus, SBMM may contribute to the spread of this noninvasive fECG technique in the
clinical practice.INGEGNERIA DELL'INFORMAZIONEAgostinelli, AngelaAgostinelli, Angel
Non-invasive procedure for fetal electrocardiography
Antenatal fetal surveillance is a field of increasing importance in modern obstetrics.
Measurements extracted (such as fetal heart rate) from antenatal fetal monitoring techniques have the potential to reduce the social, personal and financial burdens of fetal death on families, health care systems and the community.
Techniques to monitor the fetus through pregnancy have been developed with the aim of providing information to enable the clinician to diagnose fetal wellbeing, characterise
development and detect abnormality. An early diagnosis before delivery may increase the effectiveness of the appropriate treatment.
Over the years, various research efforts have been carried out in the field of fetal
electrocardiography by attaching surface electrodes to the maternal body. Unfortunately the desired fetal heartbeat signals at the electrode output are buried in an additive mixture of undesired interference disturbances.
In this thesis, a non-invasive fetal electrocardiogram machine has been designed, constructed and implemented. This machine is composed of three modified electrocardiogram circuits and an external soundcard. Data was acquired from four surface electrodes placed on the maternal body.
Eleven pregnant subjects, with a gestation age between the 30th and 40th weeks of pregnancy, were used to investigate the validity of this machine. Fetal R-waves were detected in 72.7 percent of subjects.
The development of a non-invasive machine, capable of detecting and recording valuable anatomic and electrophysiological information of a fetus, represents an important tool in clinical and investigative obstetrics
Development of a Novel Dataset and Tools for Non-Invasive Fetal Electrocardiography Research
This PhD thesis presents the development of a novel open multi-modal dataset
for advanced studies on fetal cardiological assessment, along with a set of signal
processing tools for its exploitation. The Non-Invasive Fetal Electrocardiography
(ECG) Analysis (NInFEA) dataset features multi-channel electrophysiological
recordings characterized by high sampling frequency and digital resolution,
maternal respiration signal, synchronized fetal trans-abdominal pulsed-wave
Doppler (PWD) recordings and clinical annotations provided by expert
clinicians at the time of the signal collection. To the best of our knowledge,
there are no similar dataset available.
The signal processing tools targeted both the PWD and the non-invasive
fetal ECG, exploiting the recorded dataset. About the former, the study focuses
on the processing aimed at the preparation of the signal for the automatic
measurement of relevant morphological features, already adopted in the
clinical practice for cardiac assessment. To this aim, a relevant step is the automatic
identification of the complete and measurable cardiac cycles in the PWD
videos: a rigorous methodology was deployed for the analysis of the different
processing steps involved in the automatic delineation of the PWD envelope,
then implementing different approaches for the supervised classification of the
cardiac cycles, discriminating between complete and measurable vs. malformed
or incomplete ones. Finally, preliminary measurement algorithms were also developed
in order to extract clinically relevant parameters from the PWD.
About the fetal ECG, this thesis concentrated on the systematic analysis of
the adaptive filters performance for non-invasive fetal ECG extraction processing,
identified as the reference tool throughout the thesis. Then, two studies
are reported: one on the wavelet-based denoising of the extracted fetal ECG
and another one on the fetal ECG quality assessment from the analysis of the
raw abdominal recordings.
Overall, the thesis represents an important milestone in the field, by promoting
the open-data approach and introducing automated analysis tools that
could be easily integrated in future medical devices
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