Respiration Extraction from Single-Channel ECGusing Signal-Processing Methods and Deep

LearningE. Merdjanovska∗,† and A. Rashkovska∗

∗ Department of Communication Systems, Jozef Stefan Institute, Ljubljana, Slovenia† Jozef Stefan International Postgraduate School, Ljubljana, Slovenia

E-mails:{elena.merdjanovska, aleksandra.rashkovska}@ijs.si

Abstract—The measured bio-potential on the surface of thebody can give most of the information on the health status of anindividual. Sensors that measure the ECG potential difference onthe body surface could also provide information on other vitalfunctions indirectly, like respiration, by a customized analysis ofthe ECG signal. Respiration is one of the most characteristic vitalsigns and can reflect the status of a patient or the progression ofan illness. In this paper, we utilize signal-processing and deeplearning methods for the extraction of the respiratory signalfrom the differential surface potential of a single-channel ECG.From signal processing, we investigate feature-based and filter-based methods, while from deep learning, an encoder-decoderarchitecture. Simultaneous measurements of a single-channelECG and respiration have been obtained from 61 subjects beforeand after cardiac intervention in several positions of the body.We also investigate the power of the methods for respirationextraction depending on the period (pre-/post-operation) and theposition of the body when the signal is obtained. The results showthat the deep learning approach performs better than the filter-based methods but worse than the feature-based. Moreover, weconclude that different body positions do not influence respirationextraction significantly before and after the operation.

Keywords—ECG; respiration; signal processing; deep learning

I. INTRODUCTION

Respiration is one of the most informative vital signs for thephysiological state of a person or the progression of an illness[1], [2]. Its continuous monitoring is valuable not only inhospital environments, but also during daily activities at homeor in community settings [3], for example, for remote monitor-ing of chronic obstructive pulmonary disease (COPD) [4] orsleep apnoea [5]. Wearable devices for direct measurement ofrespiration parameters are usually uncomfortable when wornfor a longer period of time [6]. However, wearable sensors formonitoring other vital signals, like electrocardiogram (ECG)and photoplethysmogram (PPG), i.e. pulse oximetry, are moreunobtrusive. Medically-certified ECG sensors for long-termmonitoring are already available on the market and are usedin various areas of everyday life [7]. By utilizing the phe-nomena that respiration modulates the ECG and PPG signals,

We acknowledge the financial support of the Slovenian Research Agencyunder Grant P2-0095 and the support of the EU H2020 project SAAM underGrant No. 769661.

respiration can be extracted from these signals without usingany additional sensors. Extracting new information from theprimary signal supports the idea of a multi-functional devicefor monitoring various vital signals by combining a minimalnumber of body sensors with different functions into a singleone [8], [9]. In this way, some of the vital signs are providedwith direct measurement and others are derived from them bycustomized analysis.

The most utilized techniques for extraction of the res-piratory signal or estimation of the respiratory rate (RR)from ECG or PPG are from the area of signal processing[10], [11]. The methods are usually based on three typesof modulation: baseline wander (BW), amplitude modulation(AM), and frequency modulation (FM). Studies have shownthat extracting respiration from ECG performs better thanextracting it from PPG [12]. Besides signal processing, inrecent years, deep learning (DL) [13], an area of machinelearning, has become increasingly popular for a wide range ofproblems. Different types of deep neural network architectureshave been developed for a wide range of problems and theywere usually first used on images, and were later adjusted forother kinds of signals and their specific applications. Deeplearning methods have been successfully used to solve manydifferent tasks of ECG analysis, such as classification [14] anddenoising [15], and also for the task of respiration extractionfrom PPG [16].

In this paper, the goal is to derive the respiratory signalfrom the differential surface potential of a single-channel ECG(ECG-derived respiration). Whereas most of the research inthe domain of respiratory information extraction from ECGis focused on estimating the respiratory rate, extracting therespiratory signal as a pattern would be valuable for a morecomprehensive evaluation of different respiratory problems[4], [5]. For this task, we utilize signal-processing algorithmsand deep learning. Simultaneous measurements of a single-channel ECG and respiration have been obtained before andafter cardiac intervention in several positions of the body. Wefurther investigate the power of the methods for respirationextraction depending on the period (pre-/post-operation) andthe position of the body when the signal is obtained. The

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paper is organized as follows: in Section II, we describe theexperimental data, and the investigated signal-processing anddeep learning methods. In Section III, we present and discussthe results. We conclude with the final remarks in Section IV.

II. MATERIAL AND METHODS

A. Experimental data

The experiments in this paper were conducted on a datasetwhich contains simultaneous ECG and respiratory signalrecordings originally collected for the study in [17]. Therespiration was recorded using a nose tip thermistor probewhile two ECG signals were obtained: modified V2 and V5leads. For this study, only the modified V5 lead ECG wasused. The subjects in the study, 61 in total, were all cardiacpatients who underwent coronary artery bypass surgery. Themeasurements were done both before and after the operationfor the same patients, ranging from 2 days before to 28 daysafter. The patients were lying in three different positions duringthe measurements: supine (on the back), left lateral recumbent(on the left side) and right lateral recumbent (on the right side).Each recording is 10 minutes long and the signals are sampledat 450 Hz.

The study resulted in 867 simultaneous ECG and respirationrecordings in total, since each of the 61 patients was recordedmultiple times, on different days and positions. However, someof the recordings were excluded from our experiments due todifferent reasons, such as low signal quality and missing partsof the signal. In the end, the data we worked on consisted of750 measurements. Table I gives the number of measurementsin each group, depending on the position of the body and timewhen the signals are obtained.

B. Signal-processing methods

The signal-processing algorithms for extraction of the res-piratory signal from ECG have been taken from the RRestproject – Respiratory Rate Estimation project to develop andassess methods for automated respiratory rate (RR) moni-toring, either from ECG or PPG [12]. The RRest projectimplements over 300 algorithms in Matlab, divided into threestages: extraction of respiratory signal, estimation of RR,and fusion of estimates. Considering the task in this work,we utilize only the algorithms for extraction of respiratorysignal from the original ECG signal. These algorithms aredivided into two groups: feature-based and filter-based. Forinvestigation on our measurements, we have selected the bestperforming feature-based and filter-based algorithms based onthe results from the main RRest study in [12]. From thefeature-based, we have investigated the following algorithms:

• ELF RSlinB FMeam FPt RDtGC EHF: difference be-tween the amplitudes of troughs and proceeding peaks,

• ELF RSlinB FMebw FPt RDtGC EHF: mean signalvalue between consecutive troughs [18],

• ELF RSlinB FMefm FPt RDtGC EHF: time intervalbetween consecutive peaks [19], [18].

From the filter-based, we have investigated the followingalgorithms:

TABLE INUMBER OF MEASUREMENTS IN DIFFERENT GROUPS: S (LAYING

SUPINE–ON THE BACK), L (LAYING ON THE LEFT SIDE), R (LAYING ONTHE RIGHT SIDE), PRE-OP (BEFORE SURGERY), POST-OP (AFTER

SURGERY)

Group Number of measurements

S 331L 149R 270Pre-op 271Post-op 479Total 750

• flt Bfi: Band-pass filter between 4 and 60 breaths perminute [20],

• flt Wam: The maximum amplitude of the ContinuousWavelet Transform (CWT) within plausible cardiac fre-quencies (30–220 beats per minute) [21],

• flt Wfm: The frequency corresponding to the maximumamplitude of the CWT within plausible cardiac frequen-cies [21].

For each algorithm, common initial and final filtering tothe signals have been applied. The initial filtering includeselimination of very low frequencies using a high-pass filter(-3 dB cutoff frequency of 4 breaths per minute). Intermediatesteps depend on the extraction technique used: feature-basedor filter-based, as described above. The final filtering includeselimination of non-respiratory frequencies using a band-passfilter (-3 dB cutoff frequencies of 4 and 60 breaths per minute).Moreover, signal quality is assessed using the algorithm de-scribed in [22]. The ECG signal is segmented into 32-secondwindows and the correlation between individual beats in awindow and the window’s average beat template is calculated.If the correlation is below an empirically determined threshold(here set to 0.66), then the segment is deemed to be of lowquality and is excluded from the analysis.

C. Deep learning

1) Encoder-decoder model: The proposed deep learningnetwork for respiration estimation is based on the topologyof an encoder-decoder [23]. The encoder section takes x(i) asinput and it produces feature vectors z(i). The decoder sectionuses z(i) as its input and produces the predicted respiratorysignal y(i)pred as output. These operations can be written as:

z(i) = Fenc(x(i); θenc)

y(i)pred = Fdec(z

(i); θdec)

where Fenc and Fdec are function representations of theencoder and the decoder with parameters θenc and θdec,respectively.

The neural network learns the parameters of both theencoder and the decoder (θenc and θdec) through back-propagation by minimizing the mean squared error. The en-coder takes the ECG signal as input and outputs featurevectors, which are representations of the ECG signal. The

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objective of the encoder part of the deep network is to findrepresentations related to the respiration. In other words, theencoder needs to learn features from the ECG, using whichthe decoder could correctly estimate and output the respiratorysignal. The decoder, on the other hand, aims to learn how thefeatures output by the encoder are related to the respiratorysignal.

2) Network Architecture: Both the encoder and the decoderof the network implemented in this work are fully convolu-tional. Due to this, the feature representations of the ECGsignal are obtained by applying many different filters to thesignal. The specific architecture used was originally appliedto images for the task of image segmentation and is knownas U-net [24]. Our implementation is a simplified version ofU-net, with fewer layers and parameters, but the concept ofshortcut connections is kept. Similar as in [16], where U-net has been implemented for extraction of the respiratorysignal from PPG signal, we implement an U-net architecturefor respiration extraction from ECG signal.

The network architecture is illustrated in Fig. 1. It consistsof 13 convolutional layers in total. The ones in the first levelhave 8 filters, the ones in the second 16, and the ones inthe third 32 filters. Each filter is a 1D convolution filter oflength 27. In addition, each convolutional layer has a L2 kernelregularizer with a factor of 0.0005. All convolutional layersare followed by a ReLU activation and Batch Normalization(BN). The encoder part performs down-sampling of the signalusing MaxPooling layers, while the decoder part performs up-sampling after each group of convolutional layers. In addition,there are two dropout layers, which reduce over-fitting. Thedropout rate is set to 0.6. All these parameter settings resultedin 91,745 trainable neural network parameters. The batch sizewas set to 256 and the network was trained for 200 epochs,with a learning rate of 0.0005. The network was implementedusing the Python library Keras and was trained on an NvidiaGeForce RTX 2080 Ti 11GB GPU.

D. Evaluation technique

For the signal-processing methods, the input ECG signalwas segmented into adjacent windows of 32 s. For eachwindow, the respiratory signal was derived and compared tothe measured one. For the neural network, a sliding windowtechnique was used, where the full recording is split into 32-second long segments, with 16 seconds overlap. Then eachof these windows was down-sampled to 1024 samples andfed as an input to the network. At the output, 32-secondlong estimation of the respiratory signal is obtained. After thetraining phase, the full respiratory signal is reconstructed.

For deep learning, we used the cross-validation evaluationtechnique. This is a common technique when training machinelearning models and enables to have each record as a testsample exactly once. Using this type of evaluation, we ensurethat our models are able to generalize well and the final resultsare an accurate estimate of the performance of the models forpreviously unseen examples. More specifically, 5-fold cross-validation was used, which means the models were always

Fig. 1. U-net architecture

trained on 80% of the full dataset and tested on the remaining20%. Furthermore, a random validation set consisting of 15%of the training set was used to select the best model for eachfold.

To evaluate the methods quantitatively, we use the correla-tion coefficient (CC) metric calculated between the measured(actual) and derived respiratory signal. This metric can give anestimate of how similar two signals are in shape; however, itis not perfect in conveying how close one signal is to another.For each investigated method, we report the average CC onall measurements and all groups of measurements.

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TABLE IICORRELATION COEFFICIENT (CC) BETWEEN THE MEASURED AND

ECG-DERIVED RESPIRATORY SIGNAL FOR THE RRESTSIGNAL-PROCESSING METHODS AND THE U-NET

Method CC

ELF RSlinB FMeam FPt RDtGC EHF 0.745765184ELF RSlinB FMebw FPt RDtGC EHF 0.646964775

Signal ELF RSlinB FMefm FPt RDtGC EHF 0.553050046processing flt Bfi 0.46416707

flt Wam 0.433004984flt Wfm 0.393748786

DL U-net 0.485849124

III. RESULTS AND DISCUSSION

The CC values for the signal-processing methods and the U-net are given in Table II. Taking into consideration the differentevaluation techniques for the RRest methods and the neuralnetwork, a direct comparison of their performance using theCC values cannot be taken as fair. Nevertheless, the resultshave shown that, from the signal-processing algorithms, thefeature-based algorithms perform better than the filter-based,with the AM feature-based algorithm achieving the best resultof 0.7458 average CC. This outcome is in agreement with thefinding made in the original study of the RRest project [12],where conclusions have been made that feature-based methodsin the stage of extraction of the respiratory signal perform thebest. The 5-fold performance of the U-net is better than theperformance of the filter-based methods, but worse than theone of the feature-based.

In order to compare the performance of the methods de-pending on when and how the measurements were obtained,the measurements have been divided into 3 groups accordingto the position of the body: S group (laying supine–on theback), L group (laying on the left side), R group (layingon the right side); and 2 groups according to the time ofthe measurement: pre-op (before surgery) and post-op (af-ter surgery). Such investigation is feasible using the signal-processing methods, because the deep learning approach hasan evaluation procedure that cannot be easily accommodatedfor such an experiment. The results for the CC of the signal-processing methods for the different groups are given in TableIII. Graphical representation of the results is given in Fig. 2.The graph reveals that when the algorithms are compared toone another for different groups, again the feature-based arebetter than the filter-based. When compared between groups,it can be seen that for almost all algorithms, except one, thebest performance is achieved for the measurements obtainedbefore the operation. Between the groups with different bodypositions during measurement, such a conclusion cannot bemade. Nevertheless, the performances for each group arerelatively very close to each other and we can conclude thatthe position and the time of the measurement do not influencemuch the extraction process. This finding is in accordancewith the conclusions made in the original study in whichthe measurements were obtained: different body positions do

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S group L group R group Pre-op Post-op

ELF_RSlinB_FMeam_FPt_RDtGC_EHF ELF_RSlinB_FMebw_FPt_RDtGC_EHFELF_RSlinB_FMefm_FPt_RDtGC_EHF flt_Bfiflt_Wam flt_Wfm

Fig. 2. Graphical plot of the correlation coefficient (CC) between themeasured and ECG-derived respiratory signal for different groups: S group(laying supine–on the back), L group (laying on the left side), R group (layingon the right side), Pre-op (before surgery), Post-op (after surgery)

not influence breathing rates significantly before and afteroperation [17].

A more visual presentation of the results is given in Fig. 3.There, we compare the actual and derived respiratory signalfor two examples with higher (CC = 0.92733) and lower (CC =0.62352) correlation, alongside with the original ECG signal.Good overlap of the actual and derived respiration can beobserved in the first example, whereas in the second example,more discrepancies are present.

IV. CONCLUSION

The goal of the paper is to assess the extraction of therespiratory signal from single-channel ECG measurements in750 concurrent respiration-ECG recordings obtained from 61subjects before and after cardiac intervention in different po-sitions of the body. The study has demonstrated the feasibilityof extracting the respiratory signal as a pattern from a single-channel ECG, which supports the multi-functional approachto monitor ECG and breathing simultaneously using a singlebio signal. For this task, we investigated the performanceof feature-based and filter-based signal-processing methods,as well as a deep learning approach using U-net neuralnetwork architecture. The experiments have shown that thebest-performing method belongs to the group of feature-basedmethods and exploits the amplitude modulation of the ECGsignal caused by respiration. The deep learning approachperforms better than the filter-based methods, but worse thanthe feature-based. The results have confirmed that differentbody positions do not influence respiration extraction signif-icantly before and after the operation. Future work includesthe evaluation of the performance of respiration extraction onmeasurements acquired under a variety of conditions differentfrom the ones in this study. Evaluating ECG-derived respira-tion techniques on measurements obtained during every-dayactivities could be a valuable task for health monitoring inhome environments.

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TABLE IIICORRELATION COEFFICIENT (CC) BETWEEN THE MEASURED AND ECG-DERIVED RESPIRATORY SIGNAL FOR DIFFERENT GROUPS: S GROUP (LAYING

SUPINE–ON THE BACK), L GROUP (LAYING ON THE LEFT SIDE), R GROUP (LAYING ON THE RIGHT SIDE), PRE-OP (BEFORE SURGERY), POST-OP (AFTERSURGERY)

Method S group L group R group Pre-op Post-op

ELF RSlinB FMeam FPt RDtGC EHF 0.751655786 0.751340606 0.73546931 0.77352044 0.729584718ELF RSlinB FMebw FPt RDtGC EHF 0.652334101 0.673325195 0.625677164 0.693534856 0.619815843ELF RSlinB FMefm FPt RDtGC EHF 0.562002058 0.537340911 0.550930482 0.581908568 0.53622641flt Bfi 0.479953607 0.448183338 0.453742745 0.397227129 0.50301551flt Wam 0.438330829 0.424482607 0.431240009 0.450998764 0.422559344flt Wfm 0.405703995 0.371696278 0.391385664 0.401517963 0.389215863

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(a) (b)Fig. 3. Examples of measured and ECG-derived respiration with (a) high correlation (CC = 0.92733). (b) lower correlation (CC = 0.62352). The input ECGsignal is displayed above.

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