Perinatal grief creates vulnerability to anxiety in subsequent pregnancy: the mediating role of bereavement-related guilt.

AIM: This study examines the associations among perinatal grief symptoms, bereavement-related guilt, and pregnancy-related anxiety in subsequent pregnancy within the framework of a hypothesised mourning model. METHOD: Pregnant women with history of a perinatal loss were recruited using convenience sampling methods and completed a questionnaire set including the Perinatal Grief Scale, Bereavement Guilt Scale, and Pregnancy-related Anxiety Scale. RESULTS: Mediation analysis was performed to evaluate the hypothesised model in a sample of pregnant women with history of a perinatal loss (N = 111). The results indicated that bereavement-related guilt functions as a mediator in the relationship between perinatal grief severity and pregnancy-related anxiety experienced in subsequent pregnancies. CONCLUSION: These findings were evaluated in light of previous studies, providing a bereavement-based perspective on the potential transmission of the mental effects of perinatal loss to subsequent pregnancy.

Associations of maternal postpartum depressive and anxiety symptoms with 4-month infant and mother self- and interactive contingency of gaze, affect, and touch.

Maternal depression and anxiety are associated with infant and mother self- and interactive difficulties. Although maternal depression and anxiety usually co-occur, studies taking this comorbidity into account are few. Despite some literature, we lack a detailed understanding of how maternal depressive and anxiety symptoms may be associated with patterns of mother-infant interaction. We examined associations of maternal postpartum depressive and anxiety symptoms with infant and mother self- and interactive patterns by conducting multi-level time-series models in a sample of 56 Turkish mothers and their 4-month infants. Time-series models assessed the temporal dynamics of interaction via infant and mother self- and interactive contingency. Videotaped face-to-face interaction was coded on a 1s time base for infant and mother gaze and facial affect, infant vocal affect, and mother touch. Results indicated that mothers with high depressive symptoms were vulnerable to infants looking away, reacting with negative touch; their infants remained affectively midrange, metaphorically distancing themselves from mothers' affect. Mothers with high anxiety symptoms were vulnerable to infants becoming facially dampened and mothers reacted with negative facial affect. Altered infant and mother self-contingency patterns were largely opposite for maternal depressive and anxiety symptoms. These patterns describe foundational processes by which maternal postpartum mood is transmitted to the infant and which may affect infant development.

Thematic Content Analysis of Repugnant Intrusions in Highly Religious Canadian and Turkish Samples.

The degree of religiosity, a culturally relevant concept, has been associated with obsessive phenomena such as obsessional symptoms, the nature of unwanted intrusive thoughts, and responses to intrusive thoughts. Although previous research reported that repugnant (i.e., sexual and religious) intrusions had the lowest endorsement rates, these were also the most difficult to control and more likely to turn into obsessions. Highly religious individuals are more likely to be distressed by repugnant intrusions as the repugnant nature of intrusive thoughts critically threatens the perceived self. Thus, individuals with high religiosity may be more likely to respond to repugnant intrusions with dysfunctional strategies and thus become more vulnerable to OCD. This study presents the endorsement rates and qualitative features of sexual and religious intrusions among highly religious Canadian and Turkish samples. Highly religious participants were interviewed using the International Intrusive Thoughts Interview Schedule (IITIS). Thematic content analysis of the IITIS data was conducted with MAXQDA. Sexual intrusion themes of Forceful Sex, Gay Sex, Immoral Sex, and Sex with Undesirable People were identified in both samples. Religious intrusion themes of Questioning, How They Are Perceived by God, Violating Religious Doctrines, Punishment by God, and Worship also emerged in both samples. The percentages of these themes suggested the presence of cross-cultural qualitative similarities and differences.

Difficulties in Emotion Regulation Mediated the Relationship Between Reflective Functioning and Borderline Personality Symptoms Among Non-Clinical Adolescents.

The literature has established the associations between reflective functioning (RF), affect regulation, and the development of borderline personality disorder (BPD) symptoms. We aimed to examine the mediator role of difficulties in emotion regulation on the relationship between RF and BPD symptoms in a non-clinical adolescent sample. The sample was composed of 546 Turkish adolescents with a mean age of 16.18 (SD = 1.67). Of the sample, 62.5% were adolescent girls and 37.5% of boys. In the present cross-sectional research, volunteer adolescents along with parental permission filled out Socio-Demographics Form, Reflective Functioning Questionnaire, Difficulties in Emotion Regulation Scale, and Borderline Personality Inventory on paper during counseling sessions at schools. Process macro was used to conduct mediation analyses. Compromised RF was related to an increase in adolescent borderline personality symptoms, both directly and indirectly via difficulties in emotion regulation. In this non-clinical adolescent sample, a lower degree of certainty, as well as a higher degree of uncertainty about the mental states, were related to a propensity to emotion dysregulation, specifically experiencing emotions less clearly, approaching emotions impulsively, and facing emotions without a modulation strategy. These associations were in turn related to an increase in borderline personality symptoms. The present research results suggest RF and emotion regulation problems as one field of early intervention for adolescents with BPD symptoms.

The Validity and Reliability Study of the Turkish Version of Client Attachment to Therapist Scale (CATS). / Danisanin Terapiste Baglanma Ölçegi (DTBÖ) Türkçe Formu'nun Geçerlik ve Güvenirlik Çalismasi.

OBJECTIVE: This study aimed to evaluate the validity and reliability of the Turkish version of Client Attachment to Therapist Scale (CATSTR) which provides a framework for measuring and conceptualizing the relationship between the therapist and the client. METHOD: The study included 191 individuals with a mean age of 24.41 years who had received a minimum of 5 and a maximum of 15 sessions of therapy for different psychological problems. All participants completed the CATS-TR, the Early Close Relationships-R (ECR-R), the Bell Object Relations Inventory (BORRTI), and the Working Alliance Inventory (WAI-SF), and a Client Information Form handed to the clients in a closed envelope by their respective therapists. RESULTS: Exploratory and Confirmatory Factor Analysis results indicated an acceptable fit for the CATS-TR which comprised the Secure, Fearful/Avoidant and Preoccupied/Merger subscales, with internal consistency levels ranging between 0.71 and 0.85. Criterion validity analyses showed that the scores on the CATS-TR Fearful/Avoidant and Preoccupied/Merger subscales correlated with the scores on the ECR-R Avoidance/Anxiety subdimesnions and the BORRTI Object Relations subdimension in the expected directions. Also, the mean score on the CATS-TR Secure Attachment subscale was a significant predictor of the therapeutic alliance assessed by the WAI-SF and its subscales. CONCLUSION: This study has demonstrated that the CATS-TR has an acceptable level of validity and reliability with results indicating its usefulness for research and clinical settings in Turkey investigating the common factors bringing about change in psychotherapy.

The Bidirectional View of Mother-Infant Interaction by Gaze and Facial Affect. / Anne-Bebek Etkilesimine Çift-Yönlü Bakis Açisi: Bakis ve Yüz Duygulanimi.

OBJECTIVE: The aim of this research was to assess gaze behaviors and facial affect expressions in mother-infant interaction within the frame of self-contingency and interactive contingency, reflecting self-regulation and interactive regulation, respectively. In Model 1, second-by-second changing gaze behaviors (on partner's face/off partner's face) and in Model 2, facial affect expressions (from positive to negative) were examined. Self-contingency reflects the variability or stability in gaze directions and facial affect expressions in each partner. Interactive contingency reflects the degree of mother-infant gaze and facial affect attunement or interactive regulation relative to each other. METHOD: Sample was composed of 56 healthy mother-infant dyads. All infants were 4 months old, and mean maternal age was 29.61 (SD=3.71). Mother-infant interactions were filmed at the lab. Interactions were coded second-by-second for mother-infant gaze behaviors and facial affect expressions with video microanalysis method. The analysis method was multilevel-multivariate time series analysis. RESULTS: According to Model 1-2, mother-infant gaze behaviors and facial affect expressions were neither too stable nor too variable, rather, the change in gaze behaviors and facial affect expressions in each partner showed predictable patterns. Mothers regulated their gaze behaviors and facial affect expressions in relation to that of their infants. Infants regulated their facial affect expressions in relation to their mothers' facial affect expressions, but infant gaze interactive contingency to mother gaze was marginally significant. CONCLUSION: In interactions, infants and mothers regulate the rhythms of their own behavior and at the same time contingently coordinate with that of the partner. This bi-directionally regulating environment is the foundation of infants' relationship expectations and bio-socialbehavioral regulation capacity, which may be related to psychopathology in future.

Managing life during the pandemic: communication strategies, mental health, and the ultimate toll of the COVID-19

Background/aim: The purpose of this review was to present the ultimate toll of the COVID-19 pandemic by focusing on the communication strategies and mental health. Materials and methods: We unsystematically reviewed the studies published between 2020 and 2021 from databases such as Google Scholar, Web of Science and ScienceDirect. Firstly, "new-normal" life challenges during the pandemic were discussed along with the public risk communication strategies. Later, mental health problems, posttraumatic growth, and protective factors were reviewed. Results: Literature highlighted that individuals mainly experience COVID-19 related fear, anxiety, stress, negative emotions and sleep problems. Furthermore, the rates of clinically significant depression, anxiety, obsessive compulsive disorder, and posttraumatic stress disorder suggest an increase. Specifically, COVID-19 stress syndrome, loneliness, and sleep problems were associated with mental health problems in the pandemic. However, some individuals seem to be resilient to the COVID-19 trauma and experience posttraumatic growth. Brief online intervention studies are promising for reducing the emotional toll of the COVID-19 as well as for making individuals more resilient. Conclusion: To conclude, the negative conditions of the pandemic seem to make some people, but not all, vulnerable to mental illness. In addition, framing the public warnings in an optimal emotional tone seems to be more effective to comply with the precautions.

Resonance based separation and energy based classification of lung sounds using tunable wavelet transform.

BACKGROUND AND OBJECTIVE: The locations and occurrence pattern of adventitious sounds in the respiratory cycle have critical diagnostic information. In a lung sound sample, the crackles and wheezes may exist individually or they may coexist in a successive/overlapping manner superimposed onto the breath noise. The performance of the linear time-frequency representation based signal decomposition methods has been limited in the crackle/wheeze separation problem due to the common signal components that may arise in both time and frequency domain. However, the proposed resonance based decomposition can be used to isolate crackles and wheezes which behave oppositely in time domain even if they share common frequency bands. METHODS: In the proposed study, crackle and/or wheeze containing synthetic and recorded lung-sound signals were decomposed by using the resonance information which is produced by joint application of the Tunable Q-factor Wavelet Transform and Morphological Component Analysis. The crackle localization and signal reconstruction performance of the proposed approach was compared with the previously suggested Independent Component Analysis and Empirical Mode Decomposition methods in a quantitative and qualitative manner. Additionally, the decomposition ability of the proposed approach was also used to discriminate crackle and wheeze waveforms in an unsupervised way by employing signal energy. RESULTS: Results have shown that the proposed approach has significant superiority over its competitors in terms of the crackle localization and signal reconstruction ability. Moreover, the calculated energy values have revealed that the transient crackles and rhythmic wheezes can be successfully decomposed into low and high resonance channels by preserving the discriminative information. CONCLUSIONS: It is concluded that previous works suffer from deforming the waveform of the crackles whose time domain parameters are vital in computerized diagnostic classification systems. Therefore, a method should provide automatic and simultaneous decomposition ability, with smaller root mean square error and higher accuracy as demonstrated by the proposed approach.

Differential Diagnosis of Asthma and COPD Based on Multivariate Pulmonary Sounds Analysis.

OBJECTIVE: Asthma and chronic obstructive pulmonary disease (COPD) can be confused in clinical diagnosis due to overlapping symptoms. The purpose of this study is to develop a method based on multivariate pulmonary sounds analysis for differential diagnosis of the two diseases. METHODS: The recorded 14-channel pulmonary sound data are mathematically modeled using multivariate (or, vector) autoregressive (VAR) model, and the model parameters are fed to the classifier. Separate classifiers are assumed for each of the six sub-phases of flow cycle, namely, early/mid/late inspiration and expiration, and the six decisions are combined to reach the final decision. Parameter classification is performed in the Bayesian framework with the assumption of Gaussian mixture model (GMM) for the likelihoods, and the six sub-phase decisions are combined by voting, where the weights are learned by a linear support vector machine (SVM) classifier. Fifty subjects are incorporated in the study, 30 being diagnosed with asthma and 20 with COPD. RESULTS: The highest accuracy of the classifier is 98 percent, corresponding to correct classification rates of 100 and 95 percent for asthma and COPD, respectively. The prominent sub-phase to differentiate between the two diseases is found to be mid-inspiration. CONCLUSION: The methodology proves to be promising in terms of asthma-COPD differentiation based on acoustic information. The results also reveal that the six sub-phases are not equally pertinent in the differentiation. SIGNIFICANCE: Pulmonary sounds analysis may be a complementary tool in clinical practice for differential diagnosis of asthma and COPD, especially in the absence of reliable spirometric testing.

Intimate Partner Violence Victimization and Perpetration in a Turkish Female Sample: Rejection Sensitivity and Hostility.

Intimate partner violence (IPV) against women is one of the most alarming social problems all over the world. Recently, IPV research focuses on the bidirectional nature of the phenomenon, which underlines that both women and men can equally be victims and perpetrators of IPV, especially in community samples. The cycle of violence theory asserts that child abuse and neglect (CAN) is a vulnerability factor for being both a victim and perpetrator of IPV while developmental mechanisms perspective assesses mediators explaining this association. The purpose of the current study was to investigate whether CAN and each type of CAN (emotional maltreatment, physical abuse and neglect, and sexual abuse) would be associated with victimization of and perpetration by women via the multiple mediator roles of rejection sensitivity and hostility after controlling for co-occurring CAN types. The sample (N = 288) included Turkish women who were currently married or in a romantic relationship or in one of these statutes in the past year. Participants were recruited via online survey to fill out the related questionnaires. The results of Bootstrap analyses showed that the total CAN scores were associated with being a victim and perpetrator of IPV in the past year. Rejection sensitivity mediated the association between CAN and being a victim of IPV while hostility was a mediator between CAN and perpetration of IPV. Childhood emotional maltreatment (CEM) was the only trauma type which predicted victimization by rejection sensitivity and perpetration by hostility above and beyond the effects of co-occurring trauma types. The present study findings contributed to the growing literature that women exposed to childhood traumas can also be perpetrators of violence that was a demonstrated risk factor for the repeated victimization. Results implied that developmental and process variables should be taken into account rather than a strict gendered analysis of IPV in research and clinical practice.

Neuroticism and Religiosity: The Role of Obsessive Beliefs, Thought-Control Strategies and Guilt in Scrupulosity and Obsessive-Compulsive Symptoms Among Muslim Undergraduates.

Neuroticism and religiosity are distal vulnerability factors for OCD phenomenon. The present study aimed to examine the roles of obsessive beliefs (OBs), thought-control strategies, and guilt in the relationship between these vulnerability factors and obsessive-compulsive symptoms (OCSs), specifically scrupulosity symptoms in a Muslim sample via SEM. The sample consisted of 273 university students who filled out a set of questionnaires. The results indicated that neuroticism and the degree of religiosity predict OBs that are positively associated with guilt and self-punishment both of which predict scrupulosity and other OCSs. Findings of the present study were discussed in the context of the related literature.

An open access database for the evaluation of respiratory sound classification algorithms.

OBJECTIVE: Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases. APPROACH: This paper describes a public respiratory sound database, which was compiled for an international competition, the first scientific challenge of the IFMBE's International Conference on Biomedical and Health Informatics. The database includes 920 recordings acquired from 126 participants and two sets of annotations. One set contains 6898 annotated respiratory cycles, some including crackles, wheezes, or a combination of both, and some with no adventitious respiratory sounds. In the other set, precise locations of 10 775 events of crackles and wheezes were annotated. MAIN RESULTS: The best system that participated in the challenge achieved an average score of 52.5% with the respiratory cycle annotations and an average score of 91.2% with the event annotations. SIGNIFICANCE: The creation and public release of this database will be useful to the research community and could bring attention to the respiratory sound classification problem.

Wheeze type classification using non-dyadic wavelet transform based optimal energy ratio technique.

BACKGROUND AND OBJECTIVE: Wheezes in pulmonary sounds are anomalies which are often associated with obstructive type of lung diseases. The previous works on wheeze-type classification focused mainly on using fixed time-frequency/scale resolution based on Fourier and wavelet transforms. The main contribution of the proposed method, in which the time-scale resolution can be tuned according to the signal of interest, is to discriminate monophonic and polyphonic wheezes with higher accuracy than previously suggested time and time-frequency/scale based methods. METHODS: An optimal Rational Dilation Wavelet Transform (RADWT) based peak energy ratio (PER) parameter selection method is proposed to discriminate wheeze types. Previously suggested Quartile Frequency Ratios, Mean Crossing Irregularity, Multiple Signal Classification, Mel-frequency Cepstrum and Dyadic Discrete Wavelet Transform approaches are also applied and the superiority of the proposed method is demonstrated in leave-one-out (LOO) and leave-one-subject-out (LOSO) cross validation schemes with support vector machine (SVM), k nearest neighbor (k-NN) and extreme learning machine (ELM) classifiers. RESULTS: The results show that the proposed RADWT based method outperforms the state-of-the-art time, frequency, time-frequency and time-scale domain approaches for all classifiers in both LOO and LOSO cross validation settings. The highest accuracy values are obtained as 86% and 82.9% in LOO and LOSO respectively when the proposed PER features are fed into SVM. CONCLUSIONS: It is concluded that time and frequency domain characteristics of wheezes are not steady and hence, tunable time-scale representations are more successful in discriminating polyphonic and monophonic wheezes when compared with conventional fixed resolution representations.

Performance comparison of wavelet based denoising methods on discontinuous adventitious lung sounds.

Crackles and their time-domain characteristics provide important clues about different lung diseases. In this paper, we aim to de-noise synthetically produced crackles under various noise levels while preserving their information bearing parts which significantly affect crackle parameters. Classical wavelet based de-noising algorithms are deteriorated by sharp-sudden noise changes and produce Gibbs like fluctuations. On the other hand, total variation based algorithms, which are capable of alleviating the drawbacks of the classical wavelet based algorithms, are failed when dealing with piecewise-smooth signals like crackles and generate unwanted flat regions on the de-noised signals. Proposed wavelet total variation based de-noising is succeed in removing undesired artefacts originating from both classical wavelet and total variation de-noising. The proposed method is compared with classical wavelet based de-noising methods in terms of root mean square error under various white Gaussian noise levels (0 - 20 dB SNR). Moreover, in order to emphasize the de-noising ability of the methods, without deforming crackle waveform, time and frequency domain representation of a noisy and de-noised crackle is validated visually.

Computerised respiratory sounds can differentiate smokers and non-smokers.

Cigarette smoking is often associated with the development of several respiratory diseases however, if diagnosed early, the changes in the lung tissue caused by smoking may be reversible. Computerised respiratory sounds have shown to be sensitive to detect changes within the lung tissue before any other measure, however it is unknown if it is able to detect changes in the lungs of healthy smokers. This study investigated the differences between computerised respiratory sounds of healthy smokers and non-smokers. Healthy smokers and non-smokers were recruited from a university campus. Respiratory sounds were recorded simultaneously at 6 chest locations (right and left anterior, lateral and posterior) using air-coupled electret microphones. Airflow (1.0-1.5 l/s) was recorded with a pneumotachograph. Breathing phases were detected using airflow signals and respiratory sounds with validated algorithms. Forty-four participants were enrolled: 18 smokers (mean age 26.2, SD = 7 years; mean FEV1 % predicted 104.7, SD = 9) and 26 non-smokers (mean age 25.9, SD = 3.7 years; mean FEV1 % predicted 96.8, SD = 20.2). Smokers presented significantly higher frequency at maximum sound intensity during inspiration [(M = 117, SD = 16.2 Hz vs. M = 106.4, SD = 21.6 Hz; t(43) = -2.62, p = 0.0081, d z  = 0.55)], lower expiratory sound intensities (maximum intensity: [(M = 48.2, SD = 3.8 dB vs. M = 50.9, SD = 3.2 dB; t(43) = 2.68, p = 0.001, d z  = -0.78)]; mean intensity: [(M = 31.2, SD = 3.6 dB vs. M = 33.7,SD = 3 dB; t(43) = 2.42, p = 0.001, d z  = 0.75)] and higher number of inspiratory crackles (median [interquartile range] 2.2 [1.7-3.7] vs. 1.5 [1.2-2.2], p = 0.081, U = 110, r = -0.41) than non-smokers. Significant differences between computerised respiratory sounds of smokers and non-smokers have been found. Changes in respiratory sounds are often the earliest sign of disease. Thus, computerised respiratory sounds might be a promising measure to early detect smoking related respiratory diseases.

Resonance based respiratory sound decomposition aiming at localization of crackles in noisy measurements.

In this work, resonance based decomposition of lung sounds that aims to separate wheeze, crackle and vesicular sounds into three individual channels while automatically localizing crackles for both synthetic and real data is presented. Previous works focus on stationary-non stationary discrimination to separate crackles and vesicular sounds disregarding wheezes which are stationary as compared to crackles. However, wheeze sounds include important cues about the underlying pathology. Using two different threshold methods and synthetic sound generation scenarios in the presence of wheezes, resonance based decomposition performs 89.5 % crackle localization recall rate for white Gaussian noise and 98.6 % crackle localization recall rate for healthy vesicular sound treated as noise at low signal-to-noise ratios. Besides, an adaptive threshold determination which is independent from the channel at which it will be applied is used and is found to be robust to noise.

A lung sound classification system based on the rational dilation wavelet transform.

In this work, a wavelet based classification system that aims to discriminate crackle, normal and wheeze lung sounds is presented. While the previous works related with this problem use constant low Q-factor wavelets, which have limited frequency resolution and can not cope with oscillatory signals, in the proposed system, the Rational Dilation Wavelet Transform, whose Q-factors can be tuned, is employed. Proposed system yields an accuracy of 95 % for crackle, 97 % for wheeze, 93.50 % for normal and 95.17 % for total sound signal types using energy feature subset and proposed approach is superior to conventional low Q-factor wavelet analysis.

A Comparison of SVM and GMM-Based Classifier Configurations for Diagnostic Classification of Pulmonary Sounds.

GOAL: The aim of this study is to find a useful methodology to classify multiple distinct pulmonary conditions including the healthy condition and various pathological types, using pulmonary sounds data. METHODS: Fourteen-channel pulmonary sounds data of 40 subjects (healthy and pathological, where the pathologies are of obstructive and restrictive types) are modeled using a second order 250-point vector autoregressive model. The estimated model parameters are fed to support vector machine and Gaussian mixture model (GMM) classifiers which are used in various configurations, resulting in eight different methodologies in total. RESULTS: Among the eight methodologies, the hierarchical GMM classifier yields the best performance with a total correct classification rate of 85%, where the term hierarchical refers here to first classifying the data into healthy and pathological classes, then the pathological class into obstructive and restrictive types. Both the sensitivity and specificity for the healthy versus pathological classification at the first stage of hierarchy are 90%. CONCLUSION: The newly proposed methodologies provide improved results compared to the previous study. The hierarchical framework is suggested for diagnostic classification of pulmonary sounds, although the algorithms are still open for further improvements. SIGNIFICANCE: This study proposes new methodologies for diagnostic classification of pulmonary sounds, and suggests using a hierarchical framework for the first time.

Feature extraction using time-frequency analysis for monophonic-polyphonic wheeze discrimination.

The aim of this study is monophonic-polyphonic wheeze episode discrimination rather than the conventional wheeze (versus non-wheeze) episode detection. We used two different methods for feature extraction to discriminate monophonic and polyphonic wheeze episodes. One of the methods is based on frequency analysis and the other is based on time analysis. Frequency analysis based method uses ratios of quartile frequencies to exploit the difference in the power spectrum. Time analysis based method uses mean crossing irregularity to exploit the difference in periodicity in the time domain. Both methods are applied on the data before and after an image processing based preprocessing step. Calculated features are used in classification both individually and in combinations. Support vector machine, k-nearest neighbor and Naive Bayesian classifiers are adopted in leave-one-out scheme. A total of 121 monophonic and 110 polyphonic wheeze episodes are used in the experiments, where the best classification performances are 71.45% for time domain based features, 68.43% for frequency domain based features, and 75.78% for a combination of selected best features.

Exploring an optimal vector autoregressive model for multi-channel pulmonary sound data.

The purpose of this study is to find a useful mathematical model for multi-channel pulmonary sound data. Vector auto-regressive (VAR) model schema is adopted and the best set of arguments, namely, the order and sample size of the model and the sampling rate of the data, is aimed to be determined. Both conventional prediction error criteria and a set of three new criteria which are derived specifically for pulmonary sound signals are used to evaluate the success of the model. In terms of these criteria, the second order 250-point model is selected to be the most descriptive VAR model for 14-channel pulmonary sound data. The preferred sampling rate is the original data acquisition rate, which is 9600 samples per second. The effect of normalization of the data with respect to the air flow is also examined. Six normalization schemes are implemented on the data prior to the model fit, and the resulting model parameters are examined in terms of the proposed criterion measures. It is concluded that normalization with respect to flow is not necessary prior to the VAR modeling of pulmonary sound data.