Classification and analysis of electrical signals in urinary bladder smooth muscle using a modified vector quantization technique

Padmakumar, Mithun; Bhuvaneshwari, K.; Manchanda, Rohit

doi:10.1109/spcom.2012.6290248

Cited by 10 publications

(10 citation statements)

References 8 publications

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“…Our approach is also shown to be effective in analyzing experimentally recorded signals, which are inherently noisy and offer greater diversity in profiles, thereby confirming the robustness of the approach. It is interesting to note from Figures 10 and 11 that the convexity vs ADP correlation seems to be in opposition to the proposed hypothesis (Padmakumar et al, 2012 ). The reason for this is beyond the scope of the current work, which purely aims at presenting an efficient approach for quantifying AP foot convexity.…”

Section: Discussionmentioning

confidence: 59%

“…For simplicity, we shall refer to convex-upward and concave-upward rise to AP peak as convex and concave AP foot, respectively. In the case of the detrusor, it has been hypothesized that the diversity in action potential shapes is owing to the variable superposition of spontaneous transient depolarizations (STDs; similar to miniature end-plate potentials in skeletal muscle) and an unmodulated AP profile (Padmakumar et al, 2012 ), as illustrated in Figure 3 . Spontaneous neurotransmitter release from parasympathetic varicosities produces STDs in DSMCs, which on crossing the AP threshold of the cell, elicits an AP.…”

Section: Introductionmentioning

confidence: 99%

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A Method for the Analysis of AP Foot Convexity: Insights into Smooth Muscle Biophysics

Appukuttan

Padmakumar

Brain

et al. 2017

Front. Bioeng. Biotechnol.

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Action potential (AP) profiles vary based on the cell type, with cells of the same type typically producing APs with similar shapes. But in certain syncytial tissues, such as the smooth muscle of the urinary bladder wall, even a single cell is known to exhibit APs with diverse profiles. The origin of this diversity is not currently understood, but is often attributed to factors such as syncytial interactions and the spatial distribution of parasympathetic nerve terminals. Thus, the profile of an action potential is determined by the inherent properties of the cell and influenced by its biophysical environment. The analysis of an AP profile, therefore, holds potential for constructing a biophysical picture of the cellular environment. An important feature of any AP is its depolarization to threshold, termed the AP foot, which holds information about the origin of the AP. Currently, there exists no established technique for the quantification of the AP foot. In this study, we explore several possible approaches for this quantification, namely, exponential fitting, evaluation of the radius of curvature, triangulation altitude, and various area based methods. We have also proposed a modified area-based approach (CX,Y) which quantifies foot convexity as the area between the AP foot and a predefined line. We assess the robustness of the individual approaches over a wide variety of signals, mimicking AP diversity. The proposed (CX,Y) method is demonstrated to be superior to the other approaches, and we demonstrate its application on experimentally recorded AP profiles. The study reveals how the quantification of the AP foot could be related to the nature of the underlying synaptic activity and help shed light on biophysical features such as the density of innervation, proximity of varicosities, size of the syncytium, or the strength of intercellular coupling within the syncytium. The work presented here is directed toward exploring these aspects, with further potential toward clinical electrodiagnostics by providing a better understanding of whole-organ biophysics.

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Section: Discussionmentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

A Method for the Analysis of AP Foot Convexity: Insights into Smooth Muscle Biophysics

Appukuttan

Padmakumar

Brain

et al. 2017

Front. Bioeng. Biotechnol.

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“…For instance, in a syncytial model that mimics the tissue faithfully, it will be possible to explain what proportion of the variation in spike shapes owes to a difference in active mechanisms per se, and what proportion to passive features of the syncytium. One of the hypotheses that can be tested is that much of the diversity in spike shapes may arise not from differences in active mechanisms, but from varying levels of superposition of a more or less stereotypical active spike with a more variable component contributed by synaptic potentials possessing a wide range of amplitudes and dynamics (Padmakumar et al 2012). The compartmental modeling approach adopted here allows easy incorporation of active channel mechanisms and associated calcium dynamics into the cells (Hines and Carnevale 2000).…”

Section: Limitations and Extensionsmentioning

confidence: 99%

A computational model of urinary bladder smooth muscle syncytium

2014

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Certain smooth muscles, such as the detrusor of the urinary bladder, exhibit a variety of spikes that differ markedly in their amplitudes and time courses. The origin of this diversity is poorly understood but is often attributed to the syncytial nature of smooth muscle and its distributed innervation. In order to help clarify such issues, we present here a three-dimensional electrical model of syncytial smooth muscle developed using the compartmental modeling technique, with special reference to the bladder detrusor. Values of model parameters were sourced or derived from experimental data. The model was validated against various modes of stimulation employed experimentally and the results were found to accord with both theoretical predictions and experimental observations. Model outputs also satisfied criteria characteristic of electrical syncytia such as correlation between the spatial spread and temporal decay of electrotonic potentials as well as positively skewed amplitude frequency histogram for sub-threshold potentials, and lead to interesting conclusions. Based on analysis of syncytia of different sizes, it was found that a size of 21-cube may be considered the critical minimum size for an electrically infinite syncytium. Set against experimental results, we conjecture the existence of electrically sub-infinite bundles in the detrusor. Moreover, the absence of coincident activity between closely spaced cells potentially implies, counterintuitively, highly efficient electrical coupling between such cells. The model thus provides a heuristic platform for the interpretation of electrical activity in syncytial tissues.

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“…It is interesting to note that the sAPs seen in the intracellular recording from a single DSM cell (DSMC) are not identical in shape [12]. The shapes of APs generated by different type of excitable cells may vary, but generally a single excitable cell, including the smooth muscle cells of other organs, always exhibit a signature AP shape.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Classification of Spontaneous Action Potentials in Urinary Bladder

Padmakumar¹

2017

IJCTE

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It is observed that the spontaneous action potentials (sAPs) observed in a urinary bladder smooth muscle cell are of different shapes. The biophysical mechanisms underlying this shape variety is not yet known. It is assumed that the syncytial properties of the smooth muscle tissue in urinary bladder affect the shape of the sAPs generated by the smooth muscle cell. Further investigation on the matter requires accurate identification of different types of sAPs observable from the detrusor smooth muscle cells. Since such a ground truth on the number of possible sAP classes is not available and the manual identification of the sAP classes from long intracellular recording is tedious and erroneous, it becomes necessary to use an unsupervised classification algorithm to classify the observed sAP signals. K-means clustering and hierarchical clustering algorithms (both agglomerative and divisive approaches) are some of such classical clustering algorithms available. Also considering the different ways in which the data can be presented (such as raw time domain data, Fourier transform, wavelet transform, and principal components), There are multiple approaches to do the signal classification. In this study, the clustering results of all these approaches are compared and the best performing methods are shortlisted. An internal measure called cluster balance was used to quantitatively evaluate the resulting clusters.Mithun Padmakumar has completed his master in biomedical engineering from IIT Bombay in 2010 and bachelor in electrical and electronic engineering from College of Engineering Trivandrum, Kerala, India, in 2008.He is currently pursuing his Ph.D. degree in the Department of Biosciences and Bioengineering, Indian Institute of Technology (IIT) Bombay. He has attended the IBRO school on computational neuroscience in Hyderabad, and worked as a research associate in the Signal Processing and Instrumentation Lab, IIT Bombay where he developed algorithms for suppressing the motion artifacts and EMG noise from the ambulatory ECG recordings. His area of interest includes biomedical signal processing and pattern recognition.

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Classification and analysis of electrical signals in urinary bladder smooth muscle using a modified vector quantization technique

Cited by 10 publications

References 8 publications

A Method for the Analysis of AP Foot Convexity: Insights into Smooth Muscle Biophysics

A Method for the Analysis of AP Foot Convexity: Insights into Smooth Muscle Biophysics

A computational model of urinary bladder smooth muscle syncytium

Unsupervised Classification of Spontaneous Action Potentials in Urinary Bladder

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