A Novel Broadband Forcecardiography Sensor for Simultaneous Monitoring of Respiration, Infrasonic Cardiac Vibrations and Heart Sounds

The precordial mechanical vibrations generated by cardiac contractions have a rich frequency spectrum. While the lowest frequencies can be palpated, the higher infrasonic frequencies are usually captured by the seismocardiogram (SCG) signal and the audible ones correspond to heart sounds. Forcecardiography (FCG) is a non-invasive technique that measures these vibrations via force sensing resistors (FSR). This study presents a new piezoelectric sensor able to record all heart vibrations simultaneously, as well as a respiration signal. The new sensor was compared to the FSR-based one to assess its suitability for FCG. An electrocardiogram (ECG) lead and a signal from an electro-resistive respiration band (ERB) were synchronously acquired as references on six healthy volunteers (4 males, 2 females) at rest. The raw signals from the piezoelectric and the FSR-based sensors turned out to be very similar. The raw signals were divided into four components: Forcerespirogram (FRG), Low-Frequency FCG (LF-FCG), High-Frequency FCG (HF-FCG) and heart sounds (HS-FCG). A beat-by-beat comparison of FCG and ECG signals was carried out by means of regression, correlation and Bland–Altman analyses, and similarly for respiration signals (FRG and ERB). The results showed that the infrasonic FCG components are strongly related to the cardiac cycle (R2 > 0.999, null bias and Limits of Agreement (LoA) of ± 4.9 ms for HF-FCG; R2 > 0.99, null bias and LoA of ± 26.9 ms for LF-FCG) and the FRG inter-breath intervals are consistent with ERB ones (R2 > 0.99, non-significant bias and LoA of ± 0.46 s). Furthermore, the piezoelectric sensor was tested against an accelerometer and an electronic stethoscope: synchronous acquisitions were performed to quantify the similarity between the signals. ECG-triggered ensemble averages (synchronized with R-peaks) of HF-FCG and SCG showed a correlation greater than 0.81, while those of HS-FCG and PCG scored a correlation greater than 0.85. The piezoelectric sensor demonstrated superior performances as compared to the FSR, providing more accurate, beat-by-beat measurements. This is the first time that a single piezoelectric sensor demonstrated the ability to simultaneously capture respiration, heart sounds, an SCG-like signal (i.e., HF-FCG) and the LF-FCG signal, which may provide information on ventricular emptying and filling events. According to these preliminary results the novel piezoelectric FCG sensor stands as a promising device for accurate, unobtrusive, long-term monitoring of cardiorespiratory functions and paves the way for a wide range of potential applications, both in the research and clinical fields. However, these results should be confirmed by further analyses on a larger cohort of subjects, possibly including also pathological patients.

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Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

npj Digital Medicine ◽

10.1038/s41746-021-00447-y ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Elisa Mejía-Mejía ◽

James M. May ◽

Mohamed Elgendi ◽

Panayiotis A. Kyriacou

Keyword(s):

Blood Pressure ◽

Heart Rate ◽

Heart Rate Variability ◽

Pulse Rate ◽

Pulse Transit Time ◽

Pulse Rate Variability ◽

Physiological Factors ◽

Ecg Signals ◽

Non Invasive ◽

Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

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Introduction to nuclear cardiology

10.1093/med/9780199206445.003.0001 ◽

2011 ◽

Author(s):

Nikant Sabharwal ◽

Chee Yee Loong ◽

Andrew Kelion

Keyword(s):

Nuclear Cardiology ◽

Cardiac Disease ◽

Imaging Technique ◽

First Century ◽

Imaging Modalities ◽

Non Invasive ◽

Early Twenty ◽

Cardiac Imaging Technique ◽

Wide Range ◽

Electrocardiogram Ecg

Introduction 2Important milestones 4Relation to other imaging modalities 6The cardiologist of the early twenty-first century takes for granted the wide range of imaging modalities at his/her disposal, but it was not always so. At the beginning of the 1970s, invasive cardiac catheterization was the only reliable cardiac imaging technique. Subsequently, nuclear cardiology investigations led the way in the non-invasive assessment of cardiac disease. Some of the principles underlying these investigations [e.g. electrocardiogram (ECG)-triggered gating] have also been of great importance in the development of other imaging modalities....

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Hyperglycemia Identification Using ECG in Deep Learning Era

Sensors ◽

10.3390/s21186263 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6263

Author(s):

Renato Cordeiro ◽

Nima Karimian ◽

Younghee Park

Keyword(s):

Deep Learning ◽

Blood Glucose Concentration ◽

Area Under The Curve ◽

High Sensitivity ◽

Physiological Signals ◽

Ecg Signals ◽

Non Invasive ◽

Learning Classifier ◽

Electrocardiogram Ecg ◽

Smart Wearable

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration.

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Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

Sensors ◽

10.3390/s21113814 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3814

Author(s):

Fangfang Jiang ◽

Yihan Zhou ◽

Tianyi Ling ◽

Yanbing Zhang ◽

Ziyu Zhu

Keyword(s):

Atrial Fibrillation ◽

Detection Methods ◽

Ecg Signals ◽

Non Invasive ◽

Cardiac Dynamics ◽

Feature Extraction And Selection ◽

Future Work ◽

Electrocardiogram Ecg ◽

Term Monitoring

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.

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New technique for detection of changes in QRS morphology of ECG signals

AJP Heart and Circulatory Physiology ◽

10.1152/ajpheart.1983.244.4.h560 ◽

1983 ◽

Vol 244 (4) ◽

pp. H560-H566

Author(s):

S. L. Blumlein ◽

G. Harvey ◽

V. K. Murthy ◽

L. J. Haywood

Keyword(s):

Low Frequency ◽

New Technique ◽

Frequency Noise ◽

Third Order ◽

Ecg Signals ◽

Qrs Morphology ◽

Ectopic Beats ◽

The Difference ◽

A New Technique ◽

Electrocardiogram Ecg

With the use of the electrocardiogram (ECG) as a prototype signal, a new technique was devised for detecting signals embedded in noise. Averaged "normal" digitized ECG signals formed a template to which subsequent ECG QRS complexes were compared. The difference between the averaged template signals and subsequent normal beats was white noise, whereas the difference between the template and ectopic beats consisted of nonrandom signal variation. The template to new signal comparison for the zero-, first-, second-, and third-order differences utilized an approximate F test. Accurate detection of abnormal signals associated with high- and low-frequency noise is accomplished with this method, and the practical clinical utility of the method is under study.

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Detection of pathological heart murmurs by feature extraction of phonocardiogram signals

Journal of Applied and Advanced Research ◽

10.21839/jaar.2017.v2i4.94 ◽

2017 ◽

Vol 2 (4) ◽

Cited By ~ 6

Author(s):

Antra Ganguly ◽

Manisha Sharma

Keyword(s):

Heart Diseases ◽

Cost Effective ◽

Heart Sounds ◽

Cardiac Auscultation ◽

Non Invasive ◽

Hearing Ability ◽

Detection And Diagnosis ◽

Electrocardiogram Ecg ◽

Pcg Signals ◽

Heart Condition

Cardiac auscultation can be perceived as method of determining the human heart condition by listening to the heart sounds. These heart sounds contain vital information related to a person’s heart condition. Any departure from the normal cardiac auscultation readings in terms of presence of additional heart sounds is indicative of an unhealthy heart. The use of Phonocardiogram (PCG)signals (i.e. the electronic recording of heart sounds) completely dismisses the limitation of relying solely on the physician’s hearing ability. At the same time, they provide with a high-fidelity representation of the heart sounds in the most cost-effective way as compared to the methods like Electrocardiogram (ECG). In this paper, a method of detection of heart ailments by extracting the features of PCG signals is proposed. The normal heart sounds, gallop rhythms and the most common pathological murmurs have been used for analysis. By analyzing these signals, early detection and diagnosis of heart diseases can be done reliably. This will not only confirm health and longevity by early diagnosis and pin-pointed prognosis, but will also be economically suitable for those who can hardly afford tests like ECG. It can also be practicable in the case of infants wherein the other non-invasive diagnosis techniques like ECG fail.

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Self-powered Flexible piezoelectric Senorbased on PbZr0.52Ti0.48O3 nanofibers for impact force monitoring and Rubber Mat Aging Assessment

Smart Materials and Structures ◽

10.1088/1361-665x/ac437f ◽

2021 ◽

Author(s):

Yuxing Ding ◽

Ranran Geng ◽

Ruijian Zhu ◽

Weimin Zhang ◽

Weijie Wang ◽

...

Keyword(s):

Impact Force ◽

Low Frequency ◽

Piezoelectric Sensor ◽

Electrospinning Technique ◽

Rubber Mat ◽

Potential Applications ◽

External Impact ◽

Curvature Deformation ◽

Force Monitoring ◽

Self Powered

Abstract In this work, a flexible piezoelectric sensor was fabricated based on PbZr0.52Ti0.48O3(PZT) nanofibers composite, and its potential applications in impact force monitoring and rubber mat aging assessment were reported. The PZT piezoelectric nanofibers with diameters of 150–260nm were prepared via electrospinning technique, showing a high piezoelectric coefficient (d33~92.5 pm/V) for piezoelectric fibers. The PZT nanofibers and carbon nanotubes(CNTs) were dispersed in polydimethylsiloxane (PDMS) to fabricate a highly stretchable and flexible impact sensor (PZT/CNTs/PDMS piezoelectric nanocomposite sensor), which showed excellent low frequency sensitivity(as low as 0.01Hz), high bending deformation sensitivity (as low as 0.192cm-1 curvature deformation with 6.64V/cm-1 sensitivity) and cycle stability under external impact force. Besides, it is the first attempt to assess railway tracks rubber mat aging based on piezoelectric nanocomposite impact sensor, and the static stiffness relative error reaches a low value of 6.91% .

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Polyploidy in Gymnosperms: Revisited

Silvae Genetica ◽

10.1515/sg-2005-0010 ◽

2005 ◽

Vol 54 (1-6) ◽

pp. 59-69 ◽

Cited By ~ 46

Author(s):

M. Raj Ahuja

Keyword(s):

Low Frequency ◽

Current Status ◽

Chromosome Behavior ◽

Very Low Frequency ◽

Juniperus Chinensis ◽

Wide Range ◽

Potential Applications ◽

Future Potential ◽

Growth Abnormalities ◽

Fitzroya Cupressoides

Abstract There are only a few natural polyploids in gymnosperms. These have been reported in Ephera spp. (Gnetales), and Juniperus chinensis ‘Pfitzeriana’ (2n = 4x = 44), Fitzroya cupressoides (2n = 4x = 44), and the only hexaploid conifer Sequoia sempervirens (2n = 6x = 66) (Coniferales). Sporadic polyploids and aneuploids occur at a very low frequency in nurseries in conifers, but most of them show growth abnormalities, remain dwarf, and may not reach maturity. One exception is an autotetraploid tree of Larix decidua (2n = 4x = 48) that has survived in a private estate in Denmark. Colchicine-induced polyploids (colchiploids) have been produced in a several genera of conifers, including, Pinus, Picea, and Larix. These colchiploids (Co) were hybridized to untreated diploids to produce C1 and C2 generations to investigate their chromosome behavior. The colchiploids showed a wide range of chromosome variability, ranging from diploids, triploids, and tetraploids, and many were mixoploids. The colchiploids also show growth retardation, remain dwarf, and their future potential applications in forestry remains uncertain. However, genetic variability in the colchiploids still offers prospects for isolating genetically stable new genotypes. Even though polyploidy is rare in extant conifers, is it possible that ancient polyploidy or paleopolyploidy, that is prevalent in angiosperms, has also played a role in the evolution of conifers. In this paper we shall review the current status of polyploidy in gymnosperms.

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Early Prediction of Non-Cardiac disorders From ECG Using Lab view

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1003.0782s519 ◽

2019 ◽

Vol 8 (2S5) ◽

pp. 13-17

Keyword(s):

Reference Signal ◽

Invasive Technique ◽

Cardiac Diseases ◽

Ecg Signal ◽

Early Prediction ◽

Non Invasive ◽

Cardiac Disorders ◽

Electrocardiogram Ecg ◽

Tolerance Method ◽

Time Acquisition

The Electrocardiogram (ECG) is one of the most basic cardiological test done for any suspected diseases related to cardiological system. Abnormalities in any other system can also be detected with change in morphology of ECG. In this paper we note the changes in morphology of ECG for prediction of non-cardiac diseases like Emphysema, CNS haemorrhage, Thyroidism, Hypokalemia and Hyperkalemia. ECG is used to predict these diseases as it is a non-invasive technique and also the morphology of ECG wave is repetitive until any abnormality manifests itself through ECG. If any of the above mentioned non-cardiac diseases occur, significant changes appear in ECG signal and with the knowledge of these changes, early clues are provided regarding the diseases which are lifesaving. This paper works on acquisition and segmentation of ECG for extraction of features that are inevitable for the prediction of above mentioned diseases. The extracted features are classified as normal or abnormal based on the comparison with the reference signal. The reference signal contains information about the normal and abnormal morphological conditions of ECG which are segmented, extracted and stored prior in the LabVIEW. The automatic prediction of non-cardiac diseases is carried out with LabVIEW through which a tolerance method is used to correctly compare and predict that particular kind of disease. This will be later extended to real-time acquisition, processing and classification. The basic motive behind this project is to create an awareness and alert the patient before the fatal stage.

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A Fetal ECG Monitoring System Based on the Android Smartphone

Sensors ◽

10.3390/s19030446 ◽

2019 ◽

Vol 19 (3) ◽

pp. 446 ◽

Cited By ~ 4

Author(s):

Li Yuan ◽

Yanchao Yuan ◽

Zhuhuang Zhou ◽

Yanping Bai ◽

Shuicai Wu

Keyword(s):

Real Time ◽

Monitoring System ◽

Sample Entropy ◽

Ecg Monitoring ◽

Fixed Point Algorithm ◽

Ecg Signals ◽

Non Invasive ◽

Fetal Ecg ◽

Fastica Algorithm ◽

Electrocardiogram Ecg

In this paper, a fetal electrocardiogram (ECG) monitoring system based on the Android smartphone was proposed. We designed a portable low-power fetal ECG collector, which collected maternal abdominal ECG signals in real time. The ECG data were sent to a smartphone client via Bluetooth. Smartphone app software was developed based on the Android system. The app integrated the fast fixed-point algorithm for independent component analysis (FastICA) and the sample entropy algorithm, for the sake of real-time extraction of fetal ECG signals from the maternal abdominal ECG signals. The fetal heart rate was computed using the extracted fetal ECG signals. Experimental results showed that the FastICA algorithm can extract a clear fetal ECG, and the sample entropy can correctly determine the channel where the fetal ECG is located. The proposed fetal ECG monitoring system may be feasible for non-invasive, real-time monitoring of fetal ECGs.

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