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BACKGROUND: The quest for improved diagnosis and treatment in home health care models has led to the development of wearable medical devices for remote vital signs monitoring. An accurate signal and a high diagnostic yield are critical for the cost-effectiveness of wearable health care monitoring systems and their widespread application in resource-constrained environments. Despite technological advances, the information acquired by these devices can be contaminated by motion artifacts (MA) leading to misdiagnosis or repeated procedures with increases in associated costs. This makes it necessary to develop methods to improve the quality of the signal acquired by these devices. OBJECTIVE: We aimed to present a novel method for electrocardiogram (ECG) signal denoising to reduce MA. We aimed to analyze the method's performance and to compare its performance to that of existing approaches. METHODS: We present the novel Redundant denoising Independent Component Analysis method for ECG signal denoising based on the redundant and simultaneous acquisition of ECG signals and movement information, multichannel processing, and performance assessment considering the information contained in the signal waveform. The method is based on data including ECG signals from the patient's chest and back, the acquisition of triaxial movement signals from inertial measurement units, a reference signal synthesized from an autoregressive model, and the separation of interest and noise sources through multichannel independent component analysis. RESULTS: The proposed method significantly reduced MA, showing better performance and introducing a smaller distortion in the interest signal compared with other methods. Finally, the performance of the proposed method was compared to that of wavelet shrinkage and wavelet independent component analysis through the assessment of signal-to-noise ratio, dynamic time warping, and a proposed index based on the signal waveform evaluation with an ensemble average ECG. CONCLUSIONS: Our novel ECG denoising method is a contribution to converting wearable devices into medical monitoring tools that can be used to support the remote diagnosis and monitoring of cardiovascular diseases. A more accurate signal substantially improves the diagnostic yield of wearable devices. A better yield improves the devices' cost-effectiveness and contributes to their widespread application.

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Wearable vital signs monitoring and specially the electrocardiogram have taken important role due to the information that provide about high-risk diseases, it has been evidenced by the needed to increase the health service coverage in home care as has been encouraged by World Health Organization. Some wearables devices have been developed to monitor the Electrocardiographic in which the location of the measurement electrodes is modified respect to the Einthoven model. However, mislocation of the electrodes on the torso can lead to the modification of acquired signals, diagnostic mistakes and misinterpretation of the information in the signal. This work presents a volume conductor evaluation and an Electrocardiographic signal waveform comparison when the location of electrodes is changed, to find a electrodes' location that reduces distortion of interest signals. In addition, effects of motion artifacts and electrodes' location on the signal acquisition are evaluated. A group of volunteers was recorded to obtain Electrocardiographic signals, the result was compared with a computational model of the heart behavior through the Ensemble Average Electrocardiographic, Dynamic Time Warping and Signal-to-Noise Ratio methods to quantitatively determine the signal distortion. It was found that while the Einthoven method is followed, it is possible to acquire the Electrocardiographic signal from the patient's torso or back without a significant difference, and the electrodes position can be moved 6 cm at most from the suggested location by the Einthoven triangle in Mason-Likar's method.

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Monitoring speech tasks with functional near-infrared spectroscopy (fNIRS) enables investigation of speech production mechanisms and informs treatment strategies for speech-related disorders such as stuttering. Unfortunately, due to movement of the temporalis muscle, speech production can induce relative movement between probe optodes and skin. These movements generate motion artifacts during speech tasks. In practice, spurious hemodynamic responses in functional activation signals arise from lack of information about the consequences of speech-related motion artifacts, as well as from lack of standardized processing procedures for fNIRS signals during speech tasks. To this end, we characterize the effects of speech production on fNIRS signals, and we introduce a systematic analysis to ameliorate motion artifacts. The study measured 50 healthy subjects performing jaw movement (JM) tasks and found that JM produces two different patterns of motion artifacts in fNIRS. To remove these unwanted contributions, we validate a hybrid motion-correction algorithm based sequentially on spline interpolation and then wavelet filtering. We compared performance of the hybrid algorithm with standard algorithms based on spline interpolation only and wavelet decomposition only. The hybrid algorithm corrected 94% of the artifacts produced by JM, and it did not lead to spurious responses in the data. We also validated the hybrid algorithm during a reading task performed under two different conditions: reading aloud and reading silently. For both conditions, we observed significant cortical activation in brain regions related to reading. Moreover, when comparing the two conditions, good agreement of spatial and temporal activation patterns was found only when data were analyzed using the hybrid approach. Overall, the study demonstrates a standardized processing scheme for fNIRS data during speech protocols. The scheme decreases spurious responses and intersubject variability due to motion artifacts.

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OBJECTIVES: To evaluate the impact of movement and motion-artefact correction systems on CBCT image quality and interpretability of simulated diagnostic tasks for aligned and lateral-offset detectors. METHODS: A human skull simulating three diagnostic tasks (implant planning in the anterior maxilla, implant planning in the left-side-mandible and mandibular molar furcation assessment in the right-side-mandible) was mounted on a robot performing six movement types. Four CBCT units were used: Cranex 3Dx (CRA), Ortophos SL (ORT), Promax 3D Mid (PRO), and X1. Protocols were tested with aligned (CRA, ORT, PRO, and X1) and lateral-offset (CRA and PRO) detectors and two motion-artefact correction systems (PRO and X1). Movements were performed at one moment-in-time (t1), for units with an aligned detector, and three moments-in-time (t1-first-half of the acquisition, t2-second-half, t3-both) for the units with a lateral-offset detector. 98 volumes were acquired. Images were scored by three observers, blinded to the unit and presence of movement, for motion-related stripe artefacts, overall unsharpness, and interpretability. Fleiss' κ was used to assess interobserver agreement. RESULTS: Interobserver agreement was substantial for all parameters (0.66-0.68). For aligned detectors, in all diagnostic tasks a motion-artefact correction system influenced image interpretability. For lateral-offset detectors, the interpretability varied according to the unit and moment-in-time, in which the movement was performed. PRO motion-artefact correction system was less effective for the offset detector than its aligned counterpart. CONCLUSION: Motion-artefact correction systems enhanced image quality and interpretability for units with aligned detectors but were less effective for those with lateral-offset detectors.

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Resumen: Objetivo: Mejorar la identificación de cimas y pies en el pulso fotopletismográfico (PPG, por sus siglas en inglés), deformado por efecto del ruido miocinético, mediante la implementación de un dedal modificado y filtrado adaptativo. Método: Se obtuvo el PPG en 10 voluntarios sanos empleando 2 sistemas de fotopletismografía colocados en el dedo índice de cada mano, y registrándolos simultáneamente durante 3 min. Durante el primer minuto de registro, ambas manos estuvieron en reposo, y durante los 2 min posteriores, solo la mano izquierda realizó movimientos cuasi-periódicos para añadir ruido miocinético. Se emplearon 2 metodologías para procesar las señales fuera de línea, en una se usó un filtro con el algoritmo de mínimos cuadrados promediados (LMS, por sus siglas en inglés) y en la otra se hizo un preprocesamiento adicional al filtrado LMS. Ambas metodologías fueron comparadas y la de menor error porcentual en la señal recuperada se utilizó para valorar la mejora en la identificación de cimas y pies del PPG. Resultados: El error promedio obtenido fue del 22.94% para la primera metodología, y del 3.72% para la segunda. Los errores en la identificación de cimas y pies antes de filtrar el PPG fueron del 24.26 y 48.39%, respectivamente, una vez filtrados, disminuyeron a 2.02 y 3.77%, respectivamente. Conclusiones: El filtrado adaptativo basado en el algoritmo LMS, más una etapa de preprocesamiento, permite atenuar el ruido miocinético en el PPG, y aumentar la efectividad en la identificación de cimas y pies de pulso, que resultan de gran importancia para una valoración médica.

Abstract: Objective: To improve the identification of peaks and feet in photoplethysmographic (PPG) pulses deformed by myokinetic noise, through the implementation of a modified fingertip and applying adaptive filtering. Method: PPG signals were recordedfrom 10 healthy volunteers using two photoplethysmography systems placed on the index finger of each hand. Recordings lasted three minutes andwere done as follows: during the first minute, both handswere at rest, and for the lasting two minutes only the left hand was allowed to make quasi-periodicmovementsin order to add myokinetic noise. Two methodologies were employed to process the signals off-line. One consisted on using an adaptive filter based onthe Least Mean Square (LMS) algorithm, and the other includeda preprocessing stage in addition to the same LMS filter. Both filtering methods were compared and the one with the lowest error was chosen to assess the improvement in the identification of peaks and feet from PPG pulses. Results: Average percentage errorsobtained wereof 22.94% with the first filtering methodology, and 3.72% withthe second one. On identifying peaks and feet from PPG pulsesbefore filtering, error percentages obtained were of 24.26% and 48.39%, respectively, and once filtered error percentageslowered to 2.02% for peaks and 3.77% for feet. Conclusions: The attenuation of myokinetic noise in PPG pulses through LMS filtering, plusa preprocessing stage, allows increasingthe effectiveness onthe identification of peaks and feet from PPG pulses, which are of great importance for medical assessment.

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OBJECTIVE: To improve the identification of peaks and feet in photoplethysmographic (PPG) pulses deformed by myokinetic noise, through the implementation of a modified fingertip and applying adaptive filtering. METHOD: PPG signals were recordedfrom 10 healthy volunteers using two photoplethysmography systems placed on the index finger of each hand. Recordings lasted three minutes andwere done as follows: during the first minute, both handswere at rest, and for the lasting two minutes only the left hand was allowed to make quasi-periodicmovementsin order to add myokinetic noise. Two methodologies were employed to process the signals off-line. One consisted on using an adaptive filter based onthe Least Mean Square (LMS) algorithm, and the other includeda preprocessing stage in addition to the same LMS filter. Both filtering methods were compared and the one with the lowest error was chosen to assess the improvement in the identification of peaks and feet from PPG pulses. RESULTS: Average percentage errorsobtained wereof 22.94% with the first filtering methodology, and 3.72% withthe second one. On identifying peaks and feet from PPG pulsesbefore filtering, error percentages obtained were of 24.26% and 48.39%, respectively, and once filtered error percentageslowered to 2.02% for peaks and 3.77% for feet. CONCLUSIONS: The attenuation of myokinetic noise in PPG pulses through LMS filtering, plusa preprocessing stage, allows increasingthe effectiveness onthe identification of peaks and feet from PPG pulses, which are of great importance for medical assessment.

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