Publications by authors named "Mikko Pankaala"

Results from two independent clinical validation studies for measuring hemodynamics at the patient's bedside using a compact finger probe are reported. Technology comprises a barometric pressure sensor, and in one implementation, additionally, an optical sensor for photoplethysmography (PPG) is developed, which can be used to measure blood pressure and analyze rhythm, including the continuous detection of atrial fibrillation. The capabilities of the technology are shown in several form factors, including a miniaturized version resembling a common pulse oximeter to which the technology could be integrated in.

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Article Synopsis
  • Heart failure (HF) is a major health issue for those over 65, and seismocardiography, which measures heart vibrations through the chest, is being explored as a noninvasive detection method.
  • A multicenter study involving both inpatient and outpatient participants in Finland and the U.S. found that smartphone-based seismocardiography can effectively distinguish HF patients from healthy controls using advanced algorithms.
  • The study involved 217 HF patients and 786 control subjects, achieving high accuracy rates (89%) for HF detection, regardless of factors like age and sex.
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The right internal jugular vein is connected to the right atrium of the heart via the superior vena cava, and consequently its pressure, known as the jugular venous pressure or the jugular venous pulse (JVP), is an important indicator of cardiac function. The JVP can be estimated visually from the neck but it is rather difficult and imprecise. In this article we propose a method to measure the JVP using a motion sensor patch attached to the neck.

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Our aim is to develop a blood pressure (BP) measurement technology that could be integrated into a finger-worn pulse oximeter, eliminating the need for a brachial cuff. We present a miniature cuffless tonometric finger probe system that uses the oscillometric method to measure BP. Our approach uses a motorized press that is used to apply pressure to the fingertip to measure BP.

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. The purpose of this research is to develop a new deep learning framework for detecting atrial fibrillation (AFib), one of the most common heart arrhythmias, by analyzing the heart's mechanical functioning as reflected in seismocardiography (SCG) and gyrocardiography (GCG) signals. Jointly, SCG and GCG constitute the concept of mechanocardiography (MCG), a method used to measure precordial vibrations with the built-in inertial sensors of smartphones.

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Despite blood pressure being one the leading modifiable risk factors for cardiovascular disease and death, it is severely under-monitored. For this challenge we propose a finger artery non-invasive tono-oscillometric monitor (FANTOM) which is an automated low-cost instrument for measuring blood pressure and hemodynamic parameters from the fingertip. The sensing technology is highly scalable and could be integrated to a pulse oximeter probe for increased patient comfort.

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Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that is, high-frequency precordial vibrations and low-frequency respiratory movements for prospective gating in positron emission tomography (PET), computed tomography (CT), and radiotherapy.

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There is an unmet clinical need for a low cost and easy to use wearable devices for continuous cardiovascular health monitoring. A flexible and wearable wristband, based on microelectromechanical sensor (MEMS) elements array was developed to support this need. The performance of the device in cardiovascular monitoring was investigated by (i) comparing the arterial pressure waveform recordings to the gold standard, invasive catheter recording ( = 18), (ii) analyzing the ability to detect irregularities of the rhythm ( = 7), and (iii) measuring the heartrate monitoring accuracy ( = 31).

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Background: In the context of monitoring dogs, usually, accelerometers have been used to measure the dog's movement activity. Here, we study another application of the accelerometers (and gyroscopes)-seismocardiography (SCG) and gyrocardiography (GCG)-to monitor the dog's heart. Together, 3-axis SCG and 3-axis GCG constitute of 6-axis mechanocardiography (MCG), which is inbuilt to most modern smartphones.

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Cardiac translational and rotational vibrations induced by left ventricular motions are measurable using joint seismocardiography (SCG) and gyrocardiography (GCG) techniques. Multi-dimensional non-invasive monitoring of the heart reveals relative information of cardiac wall motion. A single inertial measurement unit (IMU) allows capturing cardiac vibrations in sufficient details and enables us to perform patient screening for various heart conditions.

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Positron emission tomography (PET) is a non-invasive imaging technique which may be considered as the state of art for the examination of cardiac inflammation due to atherosclerosis. A fundamental limitation of PET is that cardiac and respiratory motions reduce the quality of the achieved images. Current approaches for motion compensation involve gating the PET data based on the timing of quiescent periods of cardiac and respiratory cycles.

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Gyrocardiography (GCG) is a new non-invasive technique for assessing heart motions by using a sensor of angular motion - gyroscope - attached to the skin of the chest. In this study, we conducted simultaneous recordings of electrocardiography (ECG), GCG, and echocardiography in a group of subjects consisting of nine healthy volunteer men. Annotation of underlying fiducial points in GCG is presented and compared to opening and closing points of heart valves measured by a pulse wave Doppler.

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We present a smartphone-only solution for the detection of atrial fibrillation (AFib), which utilizes the built-in accelerometer and gyroscope sensors [inertial measurement unit, (IMU)] in the detection. Depending on the patient's situation, it is possible to use the developed smartphone application either regularly or occasionally for making a measurement of the subject. The smartphone is placed on the chest of the patient who is adviced to lay down and perform a noninvasive recording, while no external sensors are needed.

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In this paper we study the feasibility of seismocardiography (SCG) for the detection of Atrial Fibrillation (AF). In this preclinical study, data acquired from one patient having paroxysmal AF (no other heart diseases) is used to introduce specific changes in SCG signal due to AF. Observed changes and phenomena create a foundation for the development of SCG-based AF detection algorithms.

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This study presents a new technique which allows identification of individual heartbeats from seismocardiograms (SCG) with high accuracy. Our method is electrocardiogram (ECG) independent and designed based upon S-transform and Shannon energy. The S-transform which is a time-frequency (TF) representation first provides frequency-dependent resolution while preserving a direct relationship with Fourier spectrum.

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The pumping action of the heart is performed by contraction of the myocardium fibers. We present a non-invasive technique named gyrocardiography (GCG) that comprises a sensor of angular motion, gyroscope, configured to obtain three-dimensional angular velocity signals. A tri-axial micro electromechanical (MEMS) gyroscope sensor was attached to the surface of the chest to obtain gyrocardiogram.

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In this paper, a novel method to detect atrial fibrillation (AFib) from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting.

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Heart rate monitoring helps in assessing the functionality and condition of the cardiovascular system. We present a new real-time applicable approach for estimating beat-to-beat time intervals and heart rate in seismocardiograms acquired from a tri-axial microelectromechanical accelerometer. Seismocardiography (SCG) is a non-invasive method for heart monitoring which measures the mechanical activity of the heart.

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The vibrations produced by the cardiovascular system that are coupled to the precordium can be noninvasively detected using accelerometers. This technique is called seismocardiography. Although clinical applications have been proposed for seismocardiography, the physiology underlying the signal is still not clear.

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Both respiratory and cardiac motions reduce the quality and consistency of medical imaging specifically in nuclear medicine imaging. Motion artifacts can be eliminated by gating the image acquisition based on the respiratory phase and cardiac contractions throughout the medical imaging procedure. Electrocardiography (ECG), 3-axis accelerometer, and respiration belt data were processed and analyzed from ten healthy volunteers.

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