PUHUJAN TEMPORAALISEN ÄÄNIALAN VISUALISOINTISOVELLUS

Kirjoituksessa esitellään uutta puhujan pragmaattisen temporaalisenäänialaprofiilin kuvantamissovellusta. Kehitystyön taustalla on Helsinginyliopiston fonetiikan laitoksessa laadittu TVRP-ohjelma, jolla voidaan kuvantaapuhujan tuottamien ilmausten temporaalinen ääniala. TVRPVA-ohjelma (1.0) onkehitetty Oulun yliopiston sähkö- ja tietotekniikan osaston MediaTeamissa. Javakielellälaadittu sovellus mahdollistaa FO-kontuuriaineiston vuorovaikutteisenvisualisoinnin ja analyysin. Temporaalista äänialaa voidaan kuvata kolmella erivisualisaatiolla, joita ovat 'contour plot', 'min-max plot' ja 'percentile plot'. Taajuusvoidaan esittää Hz- tai puolisävelasteikolla. Lisäksi käytetyn kontuuriaineistontilastollisia ominaisuuksia voidaan tarkastella myös histogrammina. Jokaisentuotetun äänialan päälle voidaan piirtää yksi tai useampi ilmaus ja näinsuhteuttaa niiden ominaisuudet äänialaan ja ilmauksen funktioihin. Halututilmaukset voidaan joko poimia listalta tai itse äänialaprofiilista. Sovellusaloja ovatainakin prosodian, puhetyylien, tunteiden ja asenteiden tutkimus, voice-tutkimus,puhujan- ja kielen tunnistus, kielten vertailu ja murretutkimus.Avainsanat: temporaalinen ääniala, prosodia, äänitutkimus, temporaalisenäänialan visualisointiohjelmaThis paper describes a new visualization application for the pragmatic temporal voicerange profile of the speaker: TVRPVA. The application is based on the TVRP programdeveloped at the Departrnent of Phonetics, University of Helsinki. The TVRPVA 1.0program was developed in MediaTeam Language and Audio Technology Group, Universityof Oulu. TVRPVA was implemented with Java language, and it enables an interactivevisualization and analysis of FO-contour data. The temporal voice range profilecan be displayed with three different visualizations: the contour plot, the min-max plotand the percentile plot. Frequency can be displayed on the Hz scale or on the semitonescale. In addition, the statistics of the contour data can be displayed as histograms. Itis possible to superimpose one or several utterances on each voice range, thus showingtheir characteristics in relation to the voice range and the functions of the utterance.The utterances can be selected from the list or from the voice range profile itself. TVRPVAhas applications at least in the following fields: research on prosody, speech styles,emotions and attitudes, voice research, recognition ofspeaker and language, comparisonof languages and study of dialects.Keywords: temporal voice range, prosody and voice research, program for the visualisationof temporal voice rang

Electrocardiogram Quality Classification based on Robust Best Subsets Linear Prediction Error

Abstract A computationally efficient electrocardiogram (EC

Prevalence and Prognostic Significance of Negative U-waves in a 12-lead Electrocardiogram in the General Population

Negative U-waves are a relatively rare finding in an electrocardiogram (ECG), but are often associated with cardiac disease. The prognostic significance of negative U-waves in the general population is unknown. We evaluated 12-lead ECGs of 6,518 adults (45% male, mean age 50.9 +/- 13.8 years) for the presence of U-waves, and followed the subjects for 24.5 +/- 10.3 years. Primary end points were all-cause mortality, cardiac mortality, and sudden cardiac death; secondary end point was hospitalization due to cardiac causes. Negative U-waves (amplitude >= 0.05 mV) were present in 231 subjects (3.5%), minor negative (amplitude 0.30). In conclusion, negative U-waves are associated with adverse events in the general population. In men, this association is independent of cardiovascular risk factors. (C) 2018 Elsevier Inc. All rights reserved.Peer reviewe

Prognostic significance of flat T-waves in the lateral leads in general population

Publisher Copyright: © 2021 Elsevier Inc.Background: Negative T-waves are associated with sudden cardiac death (SCD) risk in the general population. Whether flat T-waves also predict SCD is not known. The aim of the study was to examine the clinical characteristics and risk of SCD in general population subjects with flat T-waves. Methods: We examined the electrocardiograms of 6750 Finnish general population adults aged ≥30 years and classified the subjects into 3 groups: 1) negative T-waves with an amplitude ≥0.1 mV in ≥2 of the leads I, II, aVL, V4–V6, 2) negative or positive low amplitude T-waves with an amplitude <0.1 mV and the ratio of T-wave and R-wave <10% in ≥2 of the leads I, II, aVL, V4–V6, and 3) normal positive T-waves (not meeting the aforesaid criteria). The association between T-wave classification and SCD was assessed during a 10-year follow-up. Results: A total of 215 (3.2%) subjects had negative T-waves, 856 (12.7%) flat T-waves, and 5679 (84.1%) normal T-waves. Flat T-wave subjects were older and had more often cardiovascular morbidities compared to normal T-wave subjects, while negative T-wave subjects were the oldest and had most often cardiovascular morbidities. After adjusting for multiple factors, both flat T-waves (hazard ratio [HR] 1.81; 95% confidence interval [CI] 1.13–2.91) and negative T-waves (HR 3.27; 95% CI 1.85–5.78) associated with SCD. Conclusions: Cardiovascular risk factors and disease are common among subjects with flat T-waves, but these minor T-wave abnormalities are also independently associated with increased SCD risk.Peer reviewe

Poor R-wave progression as a predictor of sudden cardiac death in the general population and subjects with coronary artery disease

Publisher Copyright: © 2022 Heart Rhythm SocietyBackground: Poor R-wave progression (PRWP) is a common clinical finding on the standard 12-lead electrocardiogram (ECG), but its prognostic significance is unclear. Objective: The purpose of this study was to examine the prognosis associated with PRWP in terms of sudden cardiac death (SCD), cardiac death, and all-cause mortality in general population subjects with and without coronary artery disease (CAD). Methods: Data and 12-lead ECGs were collected from a Finnish general population health examination survey conducted during 1978–1980 with follow-up until 2011. The study population consisted of 6854 subjects. Main end points were SCD, cardiac death, and all-cause mortality. PRWP was defined as R-wave amplitude ≤ 0.3 mV in lead V3 and R-wave amplitude in lead V2 ≤ R-wave amplitude in lead V3. Results: PRWP occurred in 213 subjects (3.1%). During the follow-up period of 24.3 ± 10.4 years, 3723 subjects (54.3%) died. PRWP was associated with older age, higher prevalence of heart failure and CAD, and β-blocker medication. In multivariate analyses, PRWP was associated with SCD (hazard ratio [HR] 2.13; 95% confidence interval [CI] 1.34–3.39), cardiac death (HR 1.75; 95% CI 1.35–2.15), and all-cause mortality (HR 1.29; 95% CI 1.08–1.54). In the subgroup with CAD, PRWP had a stronger association with cardiac mortality (HR 1.71; 95% CI 1.19–2.46) than in the subgroup without CAD, while the association with SCD was significant only in the subgroup with CAD (HR 2.62; 95% CI 1.38–4.98). Conclusion: PRWP was associated with adverse prognosis in the general population and with SCD in subjects with CAD.Peer reviewe

Spectral data fusion for robust ECG-derived respiration with experiments in different physical activity levels

In this paper, we study instant respiratory frequency extraction using single-channel electrocardiography (ECG) during mobile conditions such as high intensity exercise or household activities. Although there are a variety of ECG-derived respiration (EDR) methods available in the literature, their performance during such activities is not very well-studied. We propose a technique to boost the robustness and reliability of widely used and computationally efficient EDR methods, aiming to qualify them for ambulatory and daily monitoring. We fuse two independent sources of respiratory information available in ECG signal, including respiratory sinus arrhythmia (RSA) and morphological change of ECG time series, to enhance the accuracy and reliability of instant breathing rate estimation during ambulatory measurements. Our experimental results show that the fusion method outperforms individual methods in four different protocols, including household and sport activities

Contribution of body movements on the heart rate variability during high intensity running

Abstract We studied the association between the heart rate variability (HRV) and the subject’s movement during high intensity running. HRV is affected by movement, and this phenomena is known as cardiolocomotor coupling (CLC). Characterization of movement related components on the HRV spectrogram is a principal step toward meaningful interpretation of autonomic nervous system (ANS) activity. According to the literature, the aliases of the first and second harmonics of the cadence frequency are the main contributors affecting HRV. Instead, we found out that there is another aliasing component containing significant power in the HRV spectrogram. The source of this component might be the arm swings, torso movement or any other mechanical movement along the horizontal axis, orthogonal to the cadence direction. Our results show that in 13 out of 22 subjects the spectral HRV component arising from the alias of the second harmonic of cadence frequency (vertical acceleration) accommodates significantly less energy than the component related to the alias of the first harmonic of horizontal acceleration. Therefore, neglecting this component and/or considering the second harmonic of the cadence frequency as more dominant one is not always a valid assumption

Optimal short distance electrode locations for impedance pneumography measurement from the frontal thoracic area

Electrical impedance pneumography signal is a valuable tool in qualifying better the person’s health condition. It can be used in monitoring of respiration rate, rhythm and tidal volume. Impedance pneumography has also the potential in ambulatory physiological monitoring systems that are increasingly often implemented using plaster-like on-body devices. In such cases, the area of electrode substrate may be limited and therefore, the electrode configuration, which is able to provide both a clinically valuable electrocardiogram signal and accurate pulmonary information, is an issue. EAS is a useful small area electrode configuration that can be used for electrocardiogram measurements. In this work, different two-electrode bipolar pairs of EAS system are tested for impedance pneumography measurements. Two additional electrodes are also considered in these tests. Our results show that the electrode pair S-A provides the most accurate respiration cycle length and is least affected by movement artifact. Additionally, the results show that this electrode pair produces the signals with highest amplitude.acceptedVersionPeer reviewe

Characterization and reduction of exercise-based motion influence on heart rate variability using accelerator signals and channel decoding in the time–frequency domain

Abstract Objective: Heart rate variability (HRV) is defined as the variation of the heart’s beat to beat time intervals. Although HRV has been studied for decades, its response to stress tests and off-rest measurements is still under investigation. In this paper, we studied the influence of motion on HRV throughout different exercise tests, including a maximal running of healthy recreational runners, cycling, and walking tests of healthy subjects. Approach: In our proposed method, we utilized the motion trajectory (which is known to exist partially in HRV) measured by a three-channel accelerator (ACC). We then estimated their shares in HRV using a wearable electrocardiogram (ECG) and an error-correcting problem formulation. In this method, we characterized the motion components of three orthogonal directions induced into the HRV signal, and then we suppressed the estimated motion artefact to construct a motion-attenuated spectrogram. Main results and Significance: Our analysis showed that HRV in the exercise context is susceptible to motion artefacts. Furthermore, the interpretation of autonomic nervous system (ANS) activity and HRV indices throughout exercise has a high margin of error depending on the intensity level, type of exercise, and motion trajectory. Our experiment on 84 healthy subjects throughout mid-intensity cycling and walking tests showed 39% and 32% influence on average, respectively. In addition, our proposed method revealed through a maximal running test with 11 runners that motion can describe on average 20%–40% of the HRV high-frequency (HF) energy at different workloads of running

Spectral fusion-based breathing frequency estimation:experiment on activities of daily living

Abstract Background: We study the estimation of breathing frequency (BF) derived from wearable single-channel ECG signal in the context of mobile daily life activities. Although respiration effects on heart rate variability and ECG morphology have been well established, studies on ECG-derived respiration in daily living settings are scarce; possibly due to considerable amount of disturbances in such data. Yet, unobtrusive BF estimation during everyday activities can provide vital information for both disease management and athletic performance optimization. Method and data: For robust ECG-derived BF estimation, we combine the respiratory information derived from R–R interval (RRI) variability and morphological scale variation of QRS complexes (MSV), acquired from ECG signals. Two different fusion techniques are applied on MSV and RRI signals: cross-power spectral density (CPSD) estimation and power spectrum multiplication (PSM). The algorithms were tested on large sets of data collected from 67 participants during office, household and sport activities, simulating daily living activities. We use spirometer reference BF to evaluate and compare our estimations made by different models. Results and conclusion: PSM acquires the least average error of BF estimation, %D²ᵟ=9.86 and %E=9.45, compared to the reference spirometer values. PSM offers approximately 25 and 75% less error in comparison with the CPSD fusion estimation and the estimation by those two exclusive sources, respectively. Our results demonstrate the superiority of both of the fusion approaches, compared to the estimation derived from either of RRI or MSV signals exclusively