ECG-based Estimation of Potassium and Calcium Concentrations: Proof of Concept with Simulated Data

Pilia, Nicolas; Mesa, María Hernández; Dössel, Olaf; Loewe, Axel

doi:10.1109/embc.2019.8857634

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…data but possibly explained by factors other than [K + ]. In [15],[52], a simulated increase in [K + ] has been shown to lead to QT shortening, which would be in line with our results. Our results on T w reduction with increasing [Ca 2+ ] are concordant with the shortening of the repolarization time reported by others[16],[50],[52].…”

supporting

confidence: 92%

“…In [15],[52], a simulated increase in [K + ] has been shown to lead to QT shortening, which would be in line with our results. Our results on T w reduction with increasing [Ca 2+ ] are concordant with the shortening of the repolarization time reported by others[16],[50],[52]. Also, our results at the cellular level are aligned with those obtained with the human ventricular AP model recently proposed by Bartolucci et al[53], which, in contrast to mostAP models, is able to reproduce a physiological APD-[Ca 2+ ] relationship.…”

supporting

confidence: 92%

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Characterization of T Wave Amplitude, Duration and Morphology Changes During Hemodialysis: Relationship With Serum Electrolyte Levels and Heart Rate

Bukhari

Palmieri

Ramírez

et al. 2021

IEEE Trans. Biomed. Eng.

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Objective: Chronic kidney disease affects 7 more than 10% of the world population. Changes in serum 8 ion concentrations increase the risk for ventricular ar-9 rhythmias and sudden cardiac death, particularly in end-10 stage renal disease (ESRD) patients. We characterized how 11 T wave amplitude, duration and morphology descriptors 12 change with variations in serum levels of potassium and 13 calcium and in heart rate, both in ESRD patients and in 14 simulated ventricular fibers. Methods: Electrocardiogram 15 (ECG) recordings from twenty ESRD patients undergoing 16 hemodialysis (HD) and pseudo-ECGs (pECGs) calculated 17 from twenty-two simulated ventricular fibers at varying 18 transmural heterogeneity levels were processed to quantify 19 T wave width (T w ), T wave slope-to-amplitude ratio (T S/A ) 20 and four indices of T wave morphological variability based 21 on time warping (d w , d NL w , d a and d NL a ). Serum potassium 22 and calcium levels and heart rate were measured along 23 HD. Results: d NL a was the marker most strongly correlated 24

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supporting

confidence: 92%

supporting

confidence: 92%

Characterization of T Wave Amplitude, Duration and Morphology Changes During Hemodialysis: Relationship With Serum Electrolyte Levels and Heart Rate

Bukhari

Palmieri

Ramírez

et al. 2021

IEEE Trans. Biomed. Eng.

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“…[22]. Pilia et al also use an artificial neural network to reconstruct the extracellular ionic concentrations for both potassium and calcium with an acceptable precision in CKD patients [23]. Besides, AI can also predict cost and mortality of patients.…”

Section: Alerting Akimentioning

confidence: 99%

Role of Artificial Intelligence in Kidney Disease

Yuan¹,

Zhang²,

Deng³

et al. 2020

Int. J. Med. Sci.

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Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.

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“…For the ionic concentration estimation, features from ECG signals were extracted as described in [7]. The following features were used for further steps: center of the T wave, amplitude of the T wave, upslope of the T wave, ratio of energy of the second half of the T wave to the energy of the whole T wave, and R amplitude.…”

Section: Lead Reductionmentioning

confidence: 99%

“…The low-pass filtered noise signals were added to each setup of the ECG simulated signals. The generation of noise signals was repeated 30 times per setup to augment the dataset as described in [7]. This allowed us to augment the data set.…”

Section: Introductionmentioning

confidence: 99%

Influence of ECG Lead Reduction Techniques for Extracellular Potassium and Calcium Concentration Estimation

Mesa

Pilia

Dössel

et al. 2019

Current Directions in Biomedical Engineering

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Chronic kidney disease (CKD) affects 13% of the worldwide population and end stage patients often receive haemodialysis treatment to control the electrolyte concentrations. The cardiovascular death rate increases by 10% - 30% in dialysis patients than in general population. To analyse possible links between electrolyte concentration variation and cardiovascular diseases, a continuous non-invasive monitoring tool enabling the estimation of potassium and calcium concentration from features of the ECG is desired. Although the ECG was shown capable of being used for this purpose, the method still needs improvement. In this study, we examine the influence of lead reduction techniques on the estimation results of serum calcium and potassium concentrations.We used simulated 12 lead ECG signals obtained using an adapted Himeno et al. model. Aiming at a precise estimation of the electrolyte concentrations, we compared the estimation based on standard ECG leads with the estimation using linearly transformed fusion signals. The transformed signals were extracted from two lead reduction techniques: principle component analysis (PCA) and maximum amplitude transformation (Max- Amp). Five features describing the electrolyte changes were calculated from the signals. To reconstruct the ionic concentrations, we applied a first and a third order polynomial regression connecting the calculated features and concentration values. Furthermore, we added 30 dB white Gaussian noise to the ECGs to imitate clinically measured signals. For the noisefree case, the smallest estimation error was achieved with a specific single lead from the standard 12 lead ECG. For example, for a first order polynomial regression, the error was 0.0003±0.0767 mmol/l (mean±standard deviation) for potassium and -0.0036±0.1710 mmol/l for calcium (Wilson lead V1). For the noisy case, the PCA signal showed the best estimation performance with an error of -0.003±0.2005 mmol/l for potassium and -0.0002±0.2040 mmol/l for calcium (both first order fit). Our results show that PCA as ECG lead reduction technique is more robust against noise than MaxAmp and standard ECG leads for ionic concentration reconstruction.

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ECG-based Estimation of Potassium and Calcium Concentrations: Proof of Concept with Simulated Data

Cited by 6 publications

References 12 publications

Characterization of T Wave Amplitude, Duration and Morphology Changes During Hemodialysis: Relationship With Serum Electrolyte Levels and Heart Rate

Characterization of T Wave Amplitude, Duration and Morphology Changes During Hemodialysis: Relationship With Serum Electrolyte Levels and Heart Rate

Role of Artificial Intelligence in Kidney Disease

Influence of ECG Lead Reduction Techniques for Extracellular Potassium and Calcium Concentration Estimation

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